Multi-modal entity alignment aims to identify equivalent entities across diverse knowledge graphs by leveraging multiple modalities of entity information. This process is crucial for the fusion of multi-modal knowledge graphs. While current research primarily investigates how to utilize side information from entity visuals, relations, and attributes, it often overlooks the significant role of entity-type information. Furthermore, multi-modal data embedding encounters noise that negatively impacts the performance of the entity alignment task. To address these gaps, this paper introduces MTCEA, a multi-modal entity alignment method guided by entity-type information. The proposed method captures the constraints associated with entities based on the entity-type information obtained from knowledge graph ontology; then, it utilizes two embedding strategies for type constraints to enhance the model’s performance in knowledge representation. This allows effective modal fusion that integrates more fine-grained semantic constraints related to types, which improves the alignment accuracy across various cross-lingual knowledge graphs. MTCEA is validated on three subsets of DBP15K. Experimental results demonstrate that our model achieves good results overall on the Hits@1, Hits@10, and MRR metrics. In an experimental setting without using entity name, MTCEA outperforms state-of-the-art baselines.

Forest fire detection and resource monitoring are the focus of forest resource protection, but they often suffer from internal attacks. Insider attackers try to get away with illegal activities such as illegal logging and encroachment on forest resources by manipulating data. Illegal activities pose a huge threat to forest resources, so defending against internal attacks is key to forest regulation systems. This paper presents a blockchain-based forest fire detection system (FFD) in a multi-party environment. FFD introduced the blockchain + database architecture for data storage and designed a hybrid encryption and decryption algorithm using CP-ABE and AES algorithms to realize data sharing and access control so as to ensure data security. The artificial intelligence technology is employed to identify fires, reducing the human factor in fires. Theoretical analysis and experimental data show that FFD has high performance and is suitable for forest fire detection and monitoring.

Addressing the challenge of untraceable malicious activities due to excessive anonymity in ring signature, we propose a new SM2-based ring signature scheme with revocability and anonymity (SMRSRA). Our scheme innovates by integrating a third-party-generated signer identity tag, a pivotal element in generating both signature values and a revocation tag within the SMRSRA. A key feature of our scheme is its revocation mechanism, which permits the third party to utilize the revocation tag, activating the anonymity revocation algorithm to reveal the signer’s identity. Furthermore, our scheme allows members to verify the third party’s actions for any malicious intent using the revocable anonymity tag. Experimental findings demonstrate that the time efficiency for signature generation and revocation in this scheme scales linearly with the number of ring members, ensuring its efficiency in scenarios involving numerous participants.

Entities refer to things that exist objectively, and entity types are concepts abstracted from entities that have the same features or properties. However, the entity types in the knowledge graph are always incomplete. Currently, the main approach for predicting missing entity types is to learn structured representations of entities and types separately, which ignores neighborhood semantic knowledge of the entity. Therefore, this paper proposes the aggregation neighborhood semantics model for type completion (ANSTC), which extracts neighborhood triple features of target entities with two attentional mechanisms. Meanwhile, the spatial mapping module in ANSTC maps entities from Cartesian coordinate to Polar coordinate system, which can map similar vectors onto a concentric circle and then rotate the angle according to the fine-grained difference to achieve entity-to-type transformation. Moreover, we add semantic features from text to the entity representations to enrich semantics. Through experimental comparison on the FB15K and YAGO43K dataset, we get similar results to the baseline. We also construct person dataset in computer domain, and the values of MRR, Hit@1, Hit@3 and Hit@10 are improved compared with the ConnectE model. The experimental results demonstrate that our model can effectively predict the fine-grained entity types in the domain dataset, and achieve state-of-the-art performance.

Multimodal entity linking aims to link mentions to target entities in the multimodal knowledge graph. The current multimodal entity linking mainly focuses on the global fusion of text and image, seldom fully exploring the correlation between modalities. In order to improve the fusion effect of multimodal feature, we propose a multimodal entity linking model based on a Multimodal Co-Attention Fusion strategy. This strategy is designed to enable text and image to guide each other for extracting features, thus making full exploration of the correlation between modalities to improve the fine-grained feature fusion effect. Furthermore, we also design a candidate entity generation strategy based on Transformer, which combines multiple candidate entity sets and adjusts the candidate entity ranking to obtain high-quality candidate entity sets. We perform experiments on domain datasets and public datasets, and the experimental results demonstrate that our model has a good performance in candidate entity generation and multimodal feature fusion, outperforming the state-of-the-art baseline models.

With the development of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in the aviation industry, a large amount of data are generated in ADS-B systems everyday. Cloud storage can satisfy the requirement of storing a large amount of ADS-B data well, however, the data stored on cloud server also raises problems about the security and confidentiality. This paper proposes a secure ADS-B data outsourcing scheme with integrity guarantee and multi-keyword retrieval (MRDP) to address these issues. In our MRDP, an index is generated for each piece of ADS-B data through Bloom filters to enable multi-keyword retrieval from clouds. Also, a unique label is produced for each piece of ADS-B data for integrity verification of query results. Our MRDP solution enjoys completeness property in that all query results with regard to the multiple keywords would be returned by the cloud server, otherwise it could be detected by the user. Security analysis indicated that our scheme offers integrity, completeness and privacy protection on outsourced ADS-B data, under the computational Diffie-Hellman (CDH) assumption and the discrete logarithm (DL) assumption. Theoretical and experimental analyses demonstrate the practicality of our proposed MRDP construction compared to existing solutions.

In e-learning, the increasing number of learning resources makes it difficult for learners to find suitable learning resources. In addition, learners may have different preferences and cognitive abilities for learning resources, where differences in learners’ cognitive abilities will lead to different importance of learning resources. Therefore, recommending personalized learning paths for learners has become a research hotspot. Considering learners’ preferences and the importance of learning resources, this paper proposes a learning path recommendation algorithm based on knowledge graph. We construct a multi-dimensional courses knowledge graph in computer field (MCCKG), and then propose a method based on graph convolutional network for modeling high-order correlations on the knowledge graph to more accurately capture learners’ preferences. Furthermore, the importance of learning resources is calculated by using the characteristics of learning resources in the MCCKG and learners’ characteristics. Finally, by weighting the two factors of learners’ preferences and the importance of learning resources, we recommend the optimal learning path for learners. Our method is evaluated from the aspects of learner’s satisfaction, algorithm effectiveness, etc. The experimental results show that the method proposed in this paper can recommend a personalized learning path to satisfy the needs of learners, thus reducing the workload of manually planning learning paths.

The knowledge graph of computer discipline domain plays a critical role in computer education, and the person event is an important part of the discipline knowledge graph. Adding person events to the graph will make the discipline knowledge graph richer and more interesting, and enhance enthusiasm of students for learning. The most crucial step in building the person event knowledge graph is the extraction of trigger words. Therefore, this paper proposes a method based on the serial fusion of gated recurrent neural network and convolutional neural network (SC-BiGRU-CNN) for person event detection in the computer discipline domain. We extract the global features of the text from the person event sentences through the BiGRU model, and input the extracted global features into the CNN model to further extract the fine-grained features of the text. And then the extracted features are used to classify the event trigger words. In addition, a dataset (CD-PED) for person event detection in the computer discipline domain is constructed to obtain trigger words and their types. We perform experiments on the public dataset MAVEN and the domain dataset CD-PED, respectively. The experimental results show that our approach has significantly improved the [Formula: see text] value compared with the baseline model on the domain dataset CD-PED.

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

In unmanned aerial vehicle networks (UAVNs), unmanned aerial vehicles with restricted computing and communication capabilities can perform tasks in collaborative manner. However, communications in UAVN confront many security issues, for example, malicious entities may launch impersonate attacks. In UAVN, the command center (CMC) needs to perform mutual authentication with unmanned aerial vehicles in clusters. The aggregator (AGT) can verify the authenticity of authentication request from CMC; then, the attested authentication request is broadcasted to the reconnaissance unmanned aerial vehicle (UAV) in the same cluster. The authentication responses from UAVs can be verified and aggregated by AGT before being sent to CMC for validation. Also, existing solutions cannot resist malicious key generation center (KGC). To address these issues, this paper proposes a pairing-free authentication scheme (CLAS) for UAVNs based on the certificateless signature technology, which supports batch verification at both AGT and CMC sides so that the verification efficiency can be improved greatly. Security analysis shows that our CLAS scheme can guarantee the unforgeability for (attested) authentication request and (aggregate) responses in all phases. Performance analysis indicates that our CLAS scheme enjoys practical efficiency.

Smart grid can greatly improve the efficiency, reliability, and sustainability of the traditional grids. In industrial smart grid, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. However, the procedure of frequently metering data collection may lead to sensitive information leakage. To address the security issues in industrial smart grid, in this article, we construct an efficient identity-based metering data aggregation scheme supporting batch verification by collector and electricity service provider, respectively, which guarantees the privacy and integrity of metering data. In our scheme, collectors are allowed to collect and aggregate the metering data of users in their respective administrative domain without compromising the confidentiality of metering data. Security analysis demonstrates that our proposed scheme is provably secure in the random oracle and satisfies the above security requirements. Performance analysis indicates that our scheme outperforms existing solutions in terms of communication and computation costs.

The connected vehicular ad hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free, since their combination brings many new security and privacy requirements for VANET applications. In this paper, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments.