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

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.

Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

Different compilers and optimization levels can be used to compile the source code. Revealed in reverse from the produced binaries, these compiler details facilitate essential binary analysis tasks, such as malware analysis and software forensics. Most existing approaches adopt a signature matching based or machine learning based strategy to identify the compiler details, showing limits in either the detection accuracy or granularity. In this work, we propose NeuralCI (Neural modeling-based Compiler Identification) to infer these compiler details including compiler family, optimization level and compiler version on individual functions. The basic idea is to formulate sequence-oriented neural networks to process normalized instruction sequences generated using a lightweight function abstraction strategy. To evaluate the performance of NeuralCI, a large dataset consisting of 854,858 unique functions collected from 19 widely used real-world projects is constructed. The experiments show that NeuralCI achieves averagely 98.6% accuracy in identifying the compiler family, 95.3% accuracy in identifying the optimization level, 88.7% accuracy in identifying the compiler version, 94.8% accuracy in identifying the compiler family and optimization level, and 83.0% accuracy in identifying all compiler components simultaneously, outperforming existing function level compiler identification methods in terms of both detection accuracy and comprehensiveness.

/pdf/fine-grained-compiler-identification-with-sequence-oriented-4194t1nor0.pdf

Fine-Grained Compiler Identification With Sequence-Oriented Neural Modeling

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

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results.

On the Connectivity of Highly Dynamic Wireless Sensor Networks in Smart Factory

Big data in the industrial Internet is a key is sue for intelligent production of human-robot collaborati on that realizes perception, analysis, orientation, decision-making, adjustment, and control. Based on the analysis of the existing industrial Internet architecture, this pap er proposes a data-driven industrial Internet architectur e that involves data awareness, aggregation, interconnect ion, and application layers. In combination with the cha racteristics of the mobile terminal intelligent manufacturing industry, a complete and practical data-driven indus trial Internet solution is given.

Research on Data-Driven Industrial Internet Solutions

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods.

A Recurrent Gaussian Process Regression Model with Composite Kernel for Industrial Process Quality Prediction

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