In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

In an edge computing environment, data can be cached on edge servers to enable fast data services for users. These edge data are subject to corruption and must be verified to ensure their integrity. Meanwhile, they are also subject to partial content changes over time. Existing edge data integrity (EDI) schemes are designed to verify edge data as a whole. They fail to accommodate partially identical edge data and consequently suffer from low verification accuracy in many real-world applications. In the meantime, their reliability is subject to compromises caused by edge servers' Byzantine behaviors. This paper presents DynaEDI+, a novel decentralized EDI scheme capable of verifying the integrity of partially identical edge data. It introduces pairing trees, a new tree-based data digest structure, to enable subtree-based content comparison, allowing precise identification of version-matched data blocks. To reduce communication overhead, DynaEDI+ transmits only root nodes instead of entire trees for verification. It also implements a series of security mechanisms to safeguard the verification process against evasion attacks, replay attacks, and theft attacks from Byzantine edge servers. Theoretical analysis proves that DynaEDI+ can effectively defend against potential threats from Byzantine edge servers. Experimental results demonstrate that DynaEDI+ achieves high accuracy in edge environments with partially identical data and Byzantine edge servers, while reducing communication overhead by an order of magnitude compared to benchmark schemes.

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK’s superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available.

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing. Benefiting from the powerful ability of deep neural networks, WSD has achieved a great advancement in recent years. Reformulating WSD as a text span extraction task is an effective approach, which accepts a sentence context of an ambiguous word together with all definitions of its candidate senses simultaneously, and requires to extract the text span corresponding with the right sense. However, the approach merely depends on a short definition to learn sense representation, which neglects abundant semantic knowledge from related senses and leads to data-inefficient learning and suboptimal WSD performance. To address the limitations, we propose a novel WSD method with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (KELESC). Specifically, a knowledge-enhanced method is proposed to enrich semantic representation by incorporating additional examples and definitions of the related senses in WordNet. Then, in order to avoid the huge computing complexity induced by the additional information, a local self-attention mechanism is utilized to constrain attention to be local, which allows longer input texts without large-scale computing burdens. Extensive experimental results demonstrate that KELESC achieves better performance than baseline models on public benchmark datasets.

Recent years have seen a large increase in the amount of false information that is posted online. Fake news are created and propagated in order to deceive users and manipulate opinions and subsequently have a negative impact on the society. The automatic detection of fake news is very challenging since some of those news are created in sophisticated ways containing text or images that have been deliberately modified. Combining information from different modalities can be very useful for determining which of the online articles are fake. In this paper, we propose a multimodal multi-image system that combines information from different modalities in order to detect fake news posted online. In particular, our system combines textual, visual and semantic information. For the textual representation we use the Bidirectional Encoder Representations from Transformers (BERT) to better capture the underlying semantic and contextual meaning of the text. For the visual representation we extract image tags from multiple images that the articles contain using the VGG-16 model. The semantic representation refers to the text-image similarity calculated using the cosine similarity between the title and image tags embeddings. Our experimental results on a real world dataset show that combining features from the different modalities is effective for fake news detection. In particular, our multimodal multi-image system significantly outperforms the BERT baseline by 4.19% and SpotFake by 5.39%.