The multi-label text classification (MLTC) task involves associating text data with multiple relevant labels. However, previous studies often overlooked the co-occurrence information of labels within text, resulting in the inability to distinguish similar labels. Moreover, these studies have underestimated the importance of label node initialization. To tackle these challenges, we propose a Label-aware Multi-semantic Fusion Network (LMFN). Our approach employs label-guided attention to learn text representations closely aligned with labels. Then, in order to obtain more refined semantic representation, the word embedding matrix is introduced to integrate features from diverse sources. Concurrently, we use joint learning network to extract label features. We first initialize label nodes and use multi-layer GCNs to capture the dependencies and higher-order information between labels. Following this, cross-attention is utilized to effectively integrate label features with internal semantics and reveal latent correlations. Comprehensive experiments on two standard datasets show that our proposed model LMFN outperforms existing methods.

Fact-checking is a highly challenging task that requires verifying the truthfulness of a claim based on multiple evidence sentences. Despite the effectiveness of existing methods, they overlook the differences in the importance of various news entities. Additionally, they fail to consider the semantic relationships between the claim and the evidence from multiple perspectives. To address these issues, we propose an Entity-aware Multi-perspective Semantic Fusion Network (EMSFN) for Fact-checking Fake News Detection. First, we introduce the Entity Attention Network to extract semantic information from claim and calculate the differences in the importance of entities. Then, we built the Multi-view Semantic Relation Extraction Network to capture the interactions between claim and evidence, extracting multi-dimensional interaction information. The proposed EMSFN calculates the contribution degree of different entities and facilitates information interaction between claim and evidence from multiple perspectives. Experiments on Snopes and PolitiFact datasets validate the effectiveness of our EMSFN.

With the rapid development of social networks, the proliferation of fake news has become a pressing global issue. Such misleading content is often fabricated through the integration of multimodal data, including text and images, leading to detrimental effects on both society and the economy. Despite significant advancements in fake news detection methods in recent years, most existing approaches primarily emphasize global semantic features, often neglecting the critical role of local semantic features in the detection process. Furthermore, when integrating multimodal features, these methods struggle to effectively capture intermodal associations, resulting in suboptimal fusion performance. To address these challenges, we propose a novel fake news detection model (CSMDF) for cross-semantic multimodal data fusion. Initially, feature encoders are utilized to extract global semantic features from both the text and image modalities of the target news. Subsequently, we design a local semantic feature extraction module to capture the local semantic features within each modality by utilizing clustering algorithms, central loss functions, fully connected layers, and other methods. Meanwhile, a feature fusion module based on a bidirectional gated recurrent unit (BiGRU) is employed to integrate both global and local semantic features, generating cross-semantic representations for each modality. Finally, we implement a Co-Attention mechanism to facilitate multimodal feature fusion by integrating cross-semantic features, establishing intermodal associations, and capturing their interactive relationships. Experimental results on real datasets demonstrate that CSMDF consistently outperforms state-of-the-art methods, enhancing multimodal fake news detection.

Social media platforms have emerged as the primary channels for the general public to access and share information. However, the rapid dissemination of news has led to the emergence of a significant amount of unverified content, posing a serious threat to media credibility and network security. The current solutions primarily focus on detecting fake news by extracting and fusing features from images and text, but they have not fully utilized the relevance within and between modalities, resulting in suboptimal fusion effects. In this paper, we propose a multimodal fusion framework via multi-attention mechanism (MFMA), which considers not only the semantic and relevance features of images, textual content, and Optical Character Recognition (OCR) text from the news, but also the multimodal relevance features between them. First, we extract text within images (OCR text) to address the issue of inadequate utilization of the visual modality in traditional methods. Second, we select more robust feature extractors to capture the independent characteristics of each modality and use a pre-trained FcaNet model to extract more comprehensive image quality features. Additionally, two relevance extraction modules are designed to achieve feature fusion within and between modalities. Finally, the combined features are fed into a classifier to determine the authenticity of the news. The experimental results and analysis indicate that the model we proposed effectively enhances the performance of fake news detection.

Multi-label text classification (MLTC) is essential in natural language processing. For real applications, MLTC is challenging when class distribution is long-tailed, that is, a few of labels (head labels) have many documents while most labels (tail labels) have a few documents. In this paper, we propose a Multi-Information Filter Encoding Network (MIFEN) to address the issue of long-tailed distribution. Specifically, MIFEN constructs Multi-Information Filter Encoder to filter useless information and optimize the text space and label space. Then, MIFEN enriches the tail label-related features by extracting filtered text-specific label information for each document and highlights significant features of the corresponding text and label information to generate the final document representation. Experiment results have demonstrated that our MIFEN outperforms better than the state-of-the-art methods.

Abstract In the era of the explosion of digital content of large‐scale self‐media, user‐friendly social platforms such as Twitter and Facebook, provide opportunities for people to express their ideas and opinions freely. Due to lack of restrictions, hateful speech and its exposure can have profound psychological impacts on society. Current social networking platform is over‐reliant on the manual check, and it is labor‐intensive and time‐consuming. Although there are many machines learning methods for the detection of hate speech, short text with character limit on social platforms is more challenging for the detection of hate speech and offensive language. To address the problem of data sparsity, we have proposed a topic memory model for hate speech and offensive language detection (abbreviated as TM‐HOL). Potential topics are generated with our encoder and decoder to enrich short text features. Two memory matrices correspond to the topic words and the text, and the hate feature matrix is used to learn the syntactic features. It is demonstrated that our proposed method is effective on three datasets, performing better weighted‐F1.

In the last few years, Massive open online courses (MOOCs) have become the major online learning method worldwide. However, the high dropout rate has severely hampered its development. But most of current machine learning, deep learning, and ensemble learning methods have deficiency in learning effective features. Therefore, this paper has proposed an early dropout prediction method, and developed a system for the personalized education guidance of students and teachers. Attention-based document representation as a vector (A-Doc2vec) is proposed to learn sequence features of course and video, and heterogeneous classification model is proposed to improve the dropout prediction accuracy. This system has improved the students’ commitment to their studies, and reduced the dropout rate of MOOC Courses.

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with$P$ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

In the context of New Generation of Information Technology (NGIT) and Emerging Engineering Education (3E) in China, it is a new research hot topic to in evaluating students involvements and skills in engineering practice. Many automated assessment systems were developed specifically for the use case of grading student work. Sometimes, it is subjective evaluation and not always correct. These systems usually emphasize the training results, but neglects the process tracking od the result. In this paper, our automated assessment and evaluation of the contribution of collaborative software engineering training is proposed to evaluate student involving in software engineering training against a rubric feedback. Contributions of our method are automated assessment of laboratory environment and collaborative software development process, and cooperative development contribution model. Finally, the results of automated assessment of workload and its balance is analyzed to illustrate the effect of our software engineering training.

Summary In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row‐based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version‐based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version‐based consistency enhances the stability of the system especially in high‐concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state‐of‐the‐art methods.

Summary Live data migration in the cloud is responsible to migrate blocks of data from one emigration node to several immigration nodes. However, live data migration strategy is a NP‐hard problem like task scheduling. Recently, in‐stream processing is a new technique to process large‐scale data nearly instantaneously. This framework works fast that all decisions are made without a continuous stream of events. In this paper, we explore a real‐time live data migration strategy with stream processing paradigm. First, the nonlinear migration cost model and balance model are introduced as the metrics to evaluate the data migration strategy. Subsequently, a live data migration strategy with particle swarm optimization (PSO) is proposed. Two improvement measures called loop context and particle grouping are proposed. As an improvement of stream processing framework, nested loop context structure is a feedback to support iterative optimization algorithm. As an improvement of PSO, grouping particles before in‐stream processing are to speed up the convergence rate of PSO. Afterwards, we rebuild stream processing framework to implement these methods. The experimental results show the best performance of our method.

Summary Replication is a method to keep the consistency of source data and target data. In our previous work of access‐aware in‐memory data cache middleware for relational databases, the data are easy to be lost in case that power cuts off. Therefore, we investigate a live data replication approach from in‐memory data cache to versioning repository in this paper. This method attempts to recover the in‐memory data cache from the versioning repository in failure of access‐aware in‐memory data cache middleware. Although the replication is not a new problem, the state of art of the replication in the context of document stores is not mature. In our paper, we propose a live data replication approach of in‐memory document stores using stream processing framework. First, we introduce cell state model to describe the replication process. To infinitely look back to any revision, we enable our proposed cell state model to support copy‐modify‐merge model to manage the changed data revisions subsequently. Finally, experimental results show that this approach is more suitable for the replication of continuous in‐stream changed data compared with MapReduce‐based batch replication.

In an era of mobile internet and big data, how to make the data access more efficient is worth researching because the mobile computing environments have some limitations such as the narrow bandwidth, the frequent disconnections of network and so on. To improve the response time of query, a good cache system is of vital importance. Besides, a good cache consistency method is essential to ensure the correctness. In this paper, a semantic cache supporting version-based consistency is proposed. This cache mainly have two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version-based consistency enhances the stability of the system especially in high-concurrency situations. Several experiments has been carried out and the results are presented to show the performance of the cache.

Based on core point evolution using water ripple model, the thought of software development methodology is that the development of a complex system is translated into the water ripple sustainable evolution of core point. However, the core point is defined from three levels, which are domain, feature and function. And it does not give a general definition of the core point. In addition, the evolution of the core point only gives a simple evolutionary model, and there is no evolutionary algorithm between the core points. To address these problems, this paper further improves the feature and function core points, and put forward the framework and level core points. Then, this paper also give the corresponding evolutionary algorithm. Based on these two points, this paper developed a prototype system in order to demonstrate the water ripple evolution of the various types of core points.

Data deduplication is the task of identifying all groups of objects within one or several data sets, respectively. However, this task will become difficult in the context of big data. To address this limitation, we propose a new schema-free data deduplication approach in parallel in the aspect of breeding data deduplication related to food safety. Although MapReduce framework enables efficient parallel execution of data-intensive tasks, it cannot find duplicates in adjacent block. Furthermore, current deduplication approaches with MapReduce are restricted to fixed sliding window. Therefore, we investigate possible solutions to improve current deduplication approaches with MapReduce, to make sliding window size adaptive using adaptive multiple duplicate count strategy with alterable window step, and find duplicates by overlapping boundary objects in adjacent blocks. Moreover, we propose a multi-pass Partition-Sort-Map-Reduce approach with adaptive sliding window to speed up the deduplication process. Finally, our experimental evaluation based on the breeding data on large datasets shows the high effectiveness and efficiency of the proposed approaches.

Currently, full-text searching can benefit from the emerging NoSQL databases and traditional indexing tools in the big data era. However, there are some drawbacks of current solutions. On one hand, the indexing documents lack of the hierarchy. On the other hand, big data have become the bottleneck of full-text searching. In the context of big data, we design a full-text searching middleware over hierarchical documents. We discuss the architecture of this middleware in detail. In addition, we propose a structure-independent hierarchical document model to present the hierarchical document. Moreover, the transformation engine is designed to translate the rich files into models. The core log event listener is responsible for capturing the changed documents and push them to the indexing storage at the same time. The experimental results show that our middleware is more advantageous than RDBMS with indexes and RDBMS with Lucene solutions.

Model transformation plays an important role in current MDD (Model Driven Development). Combined with direct model manipulation, relational algebra and template theory, this paper presents a formalizing hybrid model transformation for the design in universal collaborative system. Furthermore, it implements an extendible prototype system based on MDA paradigm called CSCWMDA. CSCWMDA specifies a model driven process that takes UML models and generates collaborative system code by hybrid model transformation. Finally, a case study of Cooperative Authoring System illustrates our approach demonstrating the hybrid MDE process. This approach removes the heterogeneity of model transformation to some extent, and at the same time it is simple and well regulated.