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As a semantic knowledge base, knowledge graph is a powerful tool for managing large-scale knowledge consists with instances, concepts and relationships between them. In view that the existing domain knowledge graphs can not obtain relationships in various structures through targeted approaches in the process of construction which resulting in insufficient knowledge utilization, this paper proposes a relationship extraction method for domain knowledge graph construction. We obtain upper and lower relationships from structured data in the classification system of network encyclopedia and semi-structured data in the classification labels of web pages, and non-superordinate relationships are extracted from unstructured text through the proposed convolution residual network based on improved cross-entropy loss function. We verify the effectiveness of the designed method by comparing with existing relationship extraction methods and constructing a food domain knowledge graph.

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.

With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning.

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers’ reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.

The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.

Agri-food trade has a profound impact on social stability and sustainable economic development. However, there are several technological problems in current agricultural product transactions. For example, it is almost impossible to improve the efficiency of transactions and maintain market stability. This paper designs a novel Food Trading System with COnsortium blockchaiN (FTSCON) to eliminate information asymmetry in the food trade, in order to establish a sustainable and credible trading environment, the system uses consortium blockchain technology to meet the challenge of different authentications and permissions for different roles in food trade. Meanwhile, we have used the online double auction mechanism to eliminate competition. We also have designed a improved Practical Byzantine Fault Tolerance (iPBFT) algorithm to improve efficiency. In addition, a case study based on a series of data from Shandong Province, China indicate that the FTSCON can achieve profit improvement of merchants. Therefore, the proposed system proved to have high commercial value.

Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks.

With the development of deep learning technologies, video face tampering technologies, represented by Deepfake, can easily generate fake face images of videos by modifying the original video with only a small amount of face images. Therefore, the detection of forged facial videos has become critical to internet content regulation. In this paper, a deep forgery face video detection method with fusion of frequency domain features and spatial domain features (FFS) is proposed to address the problem of deepfake face video detection. At first, the proposed method extracts the wavelet features of images with two-dimensional discrete wavelet transform, then extracts the multidimensional wavelet feature vectors of images according to n-level wavelet decomposition. The frequency domain features of the image extracted by the discrete Fourier transform are also cascaded and fused with the wavelet features. Besides, the proposed method can better address the overfitting problem of detection methods in practical internet application scenarios by establishing a shared updatable strategy. Finally, in order to improve the generalization ability of the detection model and address the problem that the model is vulnerable to malicious attacks under the above shared update strategy, blockchain technology is employed to implement an incentive mechanism. It can motivate participants to provide real and health data, and then achieve the establishment and maintenance of a good shared and renewable environment. Moreover, we use DeepfakeDetection and Celeb-DF datasets to conduct the experiments. Samples with different percentages of high-quality images are selected to simulate the complex environment of image quality in the Internet. Experimental results show that the proposed method can improve the performance of the face forgery detection model effectively.

Retrieving 3D models by adopting hand-drawn sketches to be the input has turned out to be a popular study topic. Most current methods are based on manually selected features and the best view produced for 3D model calculations. However, there are many problems with these methods such as distortion. For the purpose of dealing with such issues, this paper proposes a novel feature representation method to select the projection view and adapt the maxout network to the extended Siamese network architecture. In addition, the strategy is able to handle the over-fitting issue of convolutional neural networks (CNN) and mitigate the discrepancies between the 3D shape domain and the sketch. A pre-trained AlexNet was used to sketch the extract features. For 3D shapes, multiple 2D views were compiled into compact feature vectors using pre-trained multi-view CNNs. Then the Siamese convolutional neural networks were learnt for transforming the two domains’ original characteristics into nonlinear feature space, which mitigated the domain discrepancy and kept the discriminations. Two large data sets were used for experiments, and the experimental results show that the method is superior to the prior art methods in accuracy.

Among many prevalent acoustic location techniques, the location problems can be modelled as solving a linear equation. Although many mature algorithms have been developed to solve the linear equation in acoustic location applications, few of them consider the inevitable noises in a real computing system that might degrade the convergence and accuracy of the algorithms or even lead to failure. Thus, to achieve promising performance when solving a linear equation in a noisy environment, a robust Newton iterative (RNI) algorithm is proposed in this paper based on control theory. Theoretical analyses indicated that the RNI algorithm can not only suppress the constant noise to zero but also maintain convergence against an increasing linear noise and random noise. In addition, extensive simulation results compared with the classic algorithms and their last variants are provided. Among these algorithms, the RNI algorithm achieves the best robustness and accuracy in the presence of noises, while it requires a longer convergence time.