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With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated. [ABSTRACT FROM AUTHOR]

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications. [ABSTRACT FROM AUTHOR]

Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approaches is that they use one-sided sentence embedding as their final prediction vector, which degrades relation extraction performance. The innovative relation extraction model REEGAT (RoBERTa Entity Embedding and Graph Attention networks enhanced sentence representation) that we present in this paper, incorporates the concept of enhanced word embedding from graph neural networks. The model first uses RoBERTa to obtain word embedding and PyTorch embedding to obtain relation embedding. Then, the multi-headed attention mechanism in GAT (graph attention network) is introduced to weight the word embedding and relation embedding to enrich further the meaning conveyed by the word embedding. Finally, the entity embedding component is used to obtain sentence representation by pooling the word embedding from GAT and the entity embedding from named entity recognition. The weighted and pooled word embedding contains more relational information to alleviate the one-sided problem of sentence representation. The experimental findings demonstrate that our model outperforms other standard methods. [ABSTRACT FROM AUTHOR]

Private set intersection (PSI) is a valuable technique with various practical applications, including secure matching of communication packets in the Internet of Things. However, most of the currently available two-party PSI protocols are based on the oblivious transfer (OT) protocol, which is computationally expensive and results in significant communication overhead. In this paper, we propose a new coding method to design a two-party PSI protocol under the semi-honest model. We analyze possible malicious attacks and then develop a PSI protocol under the malicious model using the Paillier cryptosystem, cut-and-choose, zero-knowledge proof, and other cryptographic tools. By adopting the real/ideal model paradigm, we prove the protocol's security under the malicious model, which is more efficient compared to the existing related schemes. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing and other modern technologies, collaborative computing between data is increasing, and privacy protection and secure multi-party computation are also attracting more attention. The emergence of cloud computing provides new options for data holders to perform complex computing problems and to store images; however, data privacy issues cannot be ignored. If a graphic is encrypted and stored in the cloud, the cloud server will perform confidential similar matching when the user searches. At present, most research on searchable encryption is focused on text search, with few schemes researched on how to finish the graphic search. To solve this problem, this paper proposes a secure search protocol based on graph shape under the semi-honest model. Using the cut-choose method and zero-knowledge proof, further designs of the anti-malicious attack graphic similarity judgment system (AAJS) based on the Paillier encryption algorithm, can achieve the secure search and matching of the graph while resisting malicious adversary attacks. The proposed protocol's security is proved by the real/ideal model paradigm. This paper conducts performance analysis and experimental simulation on the existing scheme and the experiments demonstrate that the system achieves high execution efficiency. [ABSTRACT FROM AUTHOR]

At present, the amount of network equipment, servers, and network traffic is increasing exponentially, and the way in which operators allocate and efficiently utilize network resources has attracted considerable attention from traffic forecasting researchers. However, with the advent of the 5G era, network traffic has also shown explosive growth, and network complexity has increased dramatically. Accurately predicting network traffic has become a pressing issue that must be addressed. In this paper, a multilayer perceptron ensemble learning method based on convolutional neural networks (CNN) and gated recurrent units (GRU) spatiotemporal feature extraction (MECG) is proposed for network traffic prediction. First, we extract spatial and temporal features of the data by convolutional neural networks (CNN) and recurrent neural networks (RNN). Then, the extracted temporal features and spatial features are fused into new spatiotemporal features through integrated learning of a multilayer perceptron, and a spatiotemporal prediction model is built in the sequence-to-sequence framework. At the same time, the teacher forcing mechanism and attention mechanism are added to improve the accuracy and convergence speed of the model. Finally, the proposed method is compared with other deep learning models for experiments. The experimental results show that the proposed method not only has apparent advantages in accuracy but also shows some superiority in time training cost. [ABSTRACT FROM AUTHOR]

In recent years, with the development of information security, secure multi-party computation has gradually become a research hotspot in the field of privacy protection. The intersection and union computation of graphs is an important branch of secure computing geometry. At present, the intersection and union of graphs are almost designed under the semi-honest model, and few solutions are proposed under the malicious model. However, the solution under the malicious model is more secure and has important theoretical and practical significance. In this paper, the possible malicious behaviors of computing the intersection and union of graphs are analyzed. Using the Lifted-ElGamal threshold cryptosystem and zero-knowledge proof method, the secure multi-party computation algorithm of graphs' intersection and union under the malicious model is designed. The real/ideal model paradigm is used to prove the security of the algorithm, the efficiency of the algorithm is analyzed in detail, and the feasibility is verified through experiment. [ABSTRACT FROM AUTHOR]

Amid the COVID-19 pandemic, prevention and control measures became normalized, prompting the development of campuses from digital to intelligent, eventually evolving to become wise. Current cutting-edge technologies include big data, Internet of Things, cloud computing, and artificial intelligence drive campus innovation, but there are still problems of unintuitive scenes, lagging monitoring information, untimely processing, and high operation and maintenance costs. Based on this, this study proposes the use of digital twin technology to digitally construct the physical campus scene, fully digitally represent it, accurately map the physical campus to the virtual campus with real-time sensing, and remotely control it to achieve the reverse control of the twin virtual campus to the physical campus. The research is guided by the theoretical model proposed by the digital twin technology, using UAV tilt photography and 3D modelling to collaboratively build the virtual campus scene. At the design stage, the interactive channel of the system is developed based on Unity3D to the realize real-time monitoring, decision making and prevention of dual spatial data. A design scheme of the spiral optimization system life cycle is formed. The modules of the smart campus system were evaluated using a system usability scale based on student experience. The experimental results show that the virtual-real campus system can enhance school management and teaching, providing important implications for promoting the development and application of campus intelligent systems. [ABSTRACT FROM AUTHOR]

Automated industrial quality detection (QD) boosts quality-detection efficiency and reduces costs. However, current quality-detection algorithms have drawbacks such as low efficiency, easily missed detections, and false detections. We propose QD-YOLO, an attention-based method to enhance quality-detection efficiency on computer mainboards. Firstly, we propose a composite attention module for the network's backbone to highlight appropriate feature channels and improve the feature fusion structure, allowing the network to concentrate on the crucial information in the feature map. Secondly, we employ the Meta-ACON activation function to dynamically learn whether the activation function is linear or non-linear for various input data and adapt it to varied input scenarios with varying linearity. Additionally, we adopt Ghost convolution instead of ordinary convolution, using linear operations as possible to reduce the number of parameters and speed up detection. Experimental results show that our method can achieve improved real-time performance and accuracy on the self-created mainboard quality defect dataset, with a mean average precision (mAP) of 98.85% and a detection speed of 31.25 Frames Per Second (FPS). Compared with the original YOLOv5s model, the improved method improves mAP@0.5 by 2.09% and detection speed by 2.67 FPS. [ABSTRACT FROM AUTHOR]

The traffic congestion situation is an important reference indicator for the orderly control and management of traffic systems. As intelligent transport systems (ITS) become increasingly popular, the challenge of realizing real-time traffic congestion situation assessments (TCSAs) in the post-traffic era is particularly important. In this study, we propose a TCSA scheme for multi-metric fuzzy integrated evaluation based on three predicted vehicle traffic parameters for the 5G Internet of Vehicles (5G-IoV) environment, which is dedicated to accelerating the development of ITS. Firstly, the scheme uses dynamic multi-model adaptive exponential smoothing (DMMAES), which can calculate the optimal smoothing coefficients and weight of each model based on historical prediction errors to predict the average speed and traffic volume and then calculate the predicted traffic speed, traffic flow density, and road saturation of the three traffic congestion indicators. Secondly, the predicted values of the three traffic congestion indicators are used as fuzzy comprehensive evaluation, taking into account the vagueness of the traffic congestion levels, the uncertainty of the indicators, and the conflict among the indicators, using a trapezoidal affiliation function to determine the degree of affiliation of each indicator through the adaptive CRITIC method to determine the weights. Finally, the predicted traffic congestion situations are classified into five levels. The effectiveness of the scheme was verified by the measured data of Yanta North Road in Xi'an. The results showed that the traffic congestion level predicted by TCSA was basically consistent with the actual situation and had a high prediction accuracy. [ABSTRACT FROM AUTHOR]

A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods. [ABSTRACT FROM AUTHOR]

Persistent Organic Pollutants (POPs) are toxic and difficult to degrade, which will cause huge damages to human life and the ecological environment. Therefore, based on historical measurements, it is important to use intelligent methods and data analysis technologies to build an intelligent prediction system to accurately predict the future POPs concentrations in advance. This work has extremely important significance for policy formulation, human health, environmental protection and the sustainable development of society. Since the POPs concentrations sequence contains both linear and nonlinear components, this paper proposes an intelligent data analysis system combining autoregressive integrated moving average (ARIMA) and long short-term memory network (LSTM) to analyze and predict the POPs concentrations in the Great Lakes region. ARIMA is used to capture linear components while LSTM is used to process nonlinear components, which overcomes the deficiency of single models. Moreover, a one-class SVM algorithm is used to detect outliers during data preprocessing. Bayesian information criterion and grid search methods are also used to obtain the optimal parameter combinations of ARIMA and LSTM, respectively. This paper compares our intelligent data analysis system with other single baseline models by using multiple evaluation indicators and finds that our system has the smallest MAE, RMSE and SMAPE values on all datasets. Meanwhile, our system can predict the trends of concentration changes well and the predicted values are closer to true values, which prove that it can effectively improve the precision of prediction. Finally, our system is used to predict concentration values of sites in the Great Lakes region in the next 5 years. The predicted concentrations present a large fluctuation trend in each year, but the overall trend is downward. [ABSTRACT FROM AUTHOR]

Benefiting from the application of vehicle communication networks and new technologies, such as connected vehicles, video monitoring, automated vehicles and vehicle–road collaboration, traffic network data can be observed in real-time. Applied in the field of traffic control, these technologies can provide high-quality input data and make a more comprehensive evaluation of the effectiveness of traffic control. However, most of the control theories and strategies adopted by adaptive control systems cannot effectively use these real-time, high-precision data. In order to adapt to the development of the times, intersection control theory needs to be further developed. This paper reviews the intersection control strategies from many perspectives, including intelligent data-driven control, conventional timing control, induction control and model-based traffic control. There are three main directions for intersection control based on the connected vehicle environment: (1) data-driven reinforcement learning control; (2) adaptive performance optimization control; (3) research on traffic control based on the environment of connected vehicles (CV); and (4) multiple intersection control based on the CV environment. The review gives a clear view of the data-driven intelligent control theory and its application for intelligent transportation systems. [ABSTRACT FROM AUTHOR]

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality. [ABSTRACT FROM AUTHOR]

With the rise and improvement of artificial intelligence technology, the express delivery industry has become more intelligent. At the terminal of modern logistics, each dispatch center has hundreds of express mail deliveries to be dispatched every day, and the number of dispatchers is far less than the number of express mail deliveries. How to assign scientific tasks to each courier dispatch is the main target of this paper. The purpose is to make the number of tasks between the various couriers in the express center roughly the same in each cycle, so that there is a more balanced income between the couriers. In the simulation experiment, the delivery addresses are clustered according to the balanced k-means algorithm. Then, the ant colony algorithm is used to plan the delivery order of the express items in each class. Then, the time cost model is established according to the delivery distance of the express items in each class and the delivery mode of the express items to calculate the delivery time cost. Through a large amount of experimental data, the standard deviation of delivery time cost of each courier gradually decreases and tends to stabilize, which suggests that this method has a good effect on the dispatching task assignment of the express center. It can effectively make the delivery workload between the distributors roughly the same, and improve the delivery efficiency of the courier, save energy, and promote sustainable development. [ABSTRACT FROM AUTHOR]