Due to the distributed collaboration and privacy protection features, federated learning is a promising technology perform model training in virtual twins of Digital Twin for Mobile Networks (DTMN). In order enhance reliability model, it always expected that users involved have trustworthy behaviors. Yet, available trust evaluation schemes problems considering simplex factor using coarse-grained calculation method. this paper, we propose scheme DTMN, which takes direct evidence recommended...

As an important means of obtaining information marine situation, the monitoring system relying on UAV has been paid more and attention by all countries in world, demand for tasks is growing continually. In ad hoc networks, routing protocols with immutable policies that lack flexibility are generally incapable maintaining effective performance due to complicated rapidly changing environmental situation application requirements. this paper, we propose intelligent clustering approach (ICRA)...

Vehicular networks have huge potential to improve road safety and traffic efficiency, especially in the context of large models. Cloud computing can significantly performance vehicular networks, concept cloud-assisted comes into being. Reputation management plays a crucial role since it help each vehicle evaluate trustworthiness other vehicles received messages. updating is essential reputation usually done by Trusted Authority (TA) regularly after collecting, decrypting, verifying number...

Recommending cold items in recommendation systems is a longstanding challenge due to the inherent differences between warm items, which are recommended based on user behavior, and content features. To tackle this, generative models generate synthetic embeddings from features, while dropout enhance robustness of system by randomly dropping behavioral during training. However, these primarily focus handling but do not effectively address recommendations. As result, may over-recommend either or...

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved aggregating billions of neighbors.To tackle this, GNNbased CTR models usually sample hundreds neighbors out facilitate efficient online recommendations.However, sampling only small portion results severe bias and failure encompass full spectrum user or item behavioral patterns.To address this...

Image-text matching by deep models has recently made remarkable achievements in many tasks, such as image caption and search. A major challenge of the text lies that they usually have complicated underlying relations between them simply modeling may lead to suboptimal performance. In this paper, we develop a novel approach bi-directional spatial-semantic attention network, which leverages both word regions (W2R) relation visual object words (O2W) holistic framework for more effectively...

Sentiment analysis of social multimedia data has attracted extensive research interest and been applied to many tasks, such as election prediction products evaluation. one modality (e.g., text or image) broadly studied. However, not much attention paid the sentiment multimodal data. Different modalities usually have information that is complementary. Thus, it necessary learn overall by combining visual content with description. In this article, we propose a novel method—Attention-Based...

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, adoption of space–air–ground-integrated network (SAGIN) architecture in vehicular can greatly performance by leveraging respective advantages space, air, ground segments on coverage, flexibility, reliability, availability, which results (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, privacy preservation also a primary concern SAGIVNs. They conflicting...

The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based perform badly cold items which haven't emerged the training set. most common solutions are to generate embedding its content features. However, generated contents have different distribution as warm learned In this case, current methods facing an...

Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed products, tend large number succession, so the previously viewed impact on users' behavior towards following items. Therefore, traditional methods that mainly focus improving accuracy recommended are suboptimal recommendations because they may highly similar For recommendation, it is crucial consider both diversity item...

Image retrieval systems help users to browse and search among extensive images in real time. With the rise of cloud computing, tasks are usually outsourced servers. However, scenario brings a daunting challenge privacy protection as servers cannot be fully trusted. To this end, image-encryption-based privacy-preserving image (PPIR) schemes have been developed, which first extract features from cipher-images, then build models based on these features. Yet, most existing PPIR approaches...

Nowadays, detecting multimodal fake news has emerged as a foremost concern since the widespread dissemination of may incur adverse societal impact. Conventional methods generally focus on capturing linguistic and visual semantics within content, which fall short in effectively distinguishing heightened level meticulous fabrications. Recently, external knowledge is introduced to provide valuable background facts complementary facilitate detection. Nevertheless, existing knowledge-enhanced...

Travel route planning aims to map out a feasible sightseeing itinerary for traveler covering famous attractions and meeting the tourist's desire. It is very useful tourists plan their travel routes when they want at unfamiliar scenic cities. Existing methods mainly concentrate on single problem special task, but not capable of being applied other tasks. For example, previous must-visit cannot be next-point recommendation, despite these two tasks are closely related each in planning. Besides,...

As a potential application field of the sixth-generation (6G) communication technology and promising part massive Internet Things (IoT), vehicular networks have attracted considerable attention from both academia industry in recent years, where cooperative safety applications are significant branch. It is widely acknowledged that 6G able to provide high-throughput low-latency wireless capability for networks, support interconnectivity with diverse service requirements, significantly improve...

In the circumstance of social big data, sentiment analysis is attracting increasing attention for its capacity in understanding individuals' attitudes and feelings. Traditional methods focus on single modality become ineffective as enormous data are emerging websites with multiple manifestations. this article, multimodal learning approaches proposed to capture relations between image text, which only stay at region level ignore fact that channels also closely correlated semantic information....

Due to the rich spatio-temporal visual content and complex multimodal relations, Video Question Answering (VideoQA) has become a challenging task attracted increasing attention. Current methods usually leverage attention, linguistic or self-attention uncover latent correlations between video question semantics. Although these exploit interactive information different modalities improve comprehension ability, inter- intra-modality cannot be effectively integrated in uniform model. To address...

Recently, federated learning has received widespread attention, which will promote the implementation of artificial intelligence technology in various fields. Privacy-preserving technologies are applied to users' local models protect privacy. Such operations make server not see true model parameters each user, opens wider door for a malicious user upload and training result converge an ineffective model. To solve this problem, article, we propose poisoning attack defense framework horizontal...

Cross-Domain Recommendation (CDR) is capable of incorporating auxiliary information from multiple domains to advance recommendation performance. Conventional CDR methods primarily rely on overlapping users, whereby knowledge conveyed between the source and target identities belonging same natural person. However, such a heuristic assumption not universally applicable due an individual may exhibit distinct or even conflicting preferences in different domains, leading potential noises. In this...