With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding. In this study, we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work, we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks, such as long-video understanding, video question answering, and video captioning, and our model can achieve state-of-the-art performances across multiple datasets.

Summary As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

Authenticated group key agreement (AGKA) protocols protect the security of communications among a group of users. Dutta and Barua proposed a dynamic AGKA protocol, but it fails to the leaving user attacks and the attacks by two malicious users. In this paper, we propose a dynamic AGKA protocol based on the computational Diffie-Hellman problem. The proposed AGKA protocol achieves sound dynamicity and efficiency. With 10 users in a group, the improvements in computation and communication overheads achieve 82.5% and 72.6 %, respectively. Then the proposed AGKA protocol is applied to vehicular ad hoc network (VANET). SC-AGKA, a self-certified authentication and key agreement protocol for VANET is presented. Pseudonyms are employed with care in SC-AGKA to provide conditional privacy. It is suitable for SC-AGKA to protect the vehicular group communications in VANET considering its security strength and efficiency.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly growing data, due to insufficient and unreliable labels and standards, and diversity and semantic ambiguity of news contents. Recently, machine learning models have been well developed to address these issues, but suffer from limited effectiveness. A unified framework is also required for them to represent various entities and relationships involved in news stories. This article proposes an entity ontology-based knowledge graph network (EKNet) to leverage knowledge graphs and entity frameworks for news credibility assessment. The model utilizes the information from knowledge graphs by combining entities and relationships from news and knowledge graphs. Experimental results show that the EKNet has advantages in evaluating news credibility over existing methods. Specifically, compared to several strong baselines, the model demonstrates a significant performance improvement in scores across various tasks. Which indicates that using the EKNet to address the challenges in news credibility assessment is highly effective and can conduct better performance for the problem of fake news in the social media environment.

NLPers frequently face reproducibility crisis in a comparison of various models of a realworld NLP task.Many studies have empirically showed that the standard splits tend to produce low reproducible and unreliable conclusions, and they attempted to improve the splits by using more random repetitions.However, the improvement on the reproducibility in a comparison of NLP models is limited attributed to a lack of investigation on the relationship between the reproducibility and the estimator induced by a splitting strategy.In this paper, we formulate the reproducibility in a model comparison into a probabilistic function with regard to a conclusion.Furthermore, we theoretically illustrate that the reproducibility is qualitatively dominated by the signal-tonoise ratio (SNR) of a model performance estimator obtained on a corpus splitting strategy.Specifically, a higher value of the SNR of an estimator probably indicates a better reproducibility.On the basis of the theoretical motivations, we develop a novel mixture estimator of the performance of an NLP model with a regularized corpus splitting strategy based on a blocked 3 × 2 cross-validation.We conduct numerical experiments on multiple NLP tasks to show that the proposed estimator achieves a high SNR, and it substantially increases the reproducibility.Therefore, we recommend the NLP practitioners to use the proposed method to compare NLP models instead of the methods based on the widely-used standard splits and the random splits with multiple repetitions.

Deep probabilistic aspect models are widely utilized in document analysis to extract the semantic information and obtain descriptive topics. However, there are two problems that may affect their applications. One is that common words shared among all documents with low representational meaning may reduce the representation ability of learned topics. The other is introducing supervision information to hierarchical topic models to fully utilize the side information of documents that is difficult. To address these problems, in this article, we first propose deep diverse latent Dirichlet allocation (DDLDA), a deep hierarchical topic model that can yield more meaningful semantic topics with less common and meaningless words by introducing shared topics. Moreover, we develop a variational inference network for DDLDA, which helps us to further generalize DDLDA to a supervised deep topic model called max-margin DDLDA (mmDDLDA) by employing max-margin principle as the classification criterion. Compared to DDLDA, mmDDLDA can discover more discriminative topical representations. In addition, a continual hybrid method with stochastic-gradient MCMC and variational inference is put forward for deep latent Dirichlet allocation (DLDA)-based models to make them more practical in real-world applications. The experimental results demonstrate that DDLDA and mmDDLDA are more efficient than existing unsupervised and supervised topic models in discovering highly discriminative topic representations and achieving higher classification accuracy. Meanwhile, DLDA and our proposed models trained by the proposed continual learning approach cannot only show good performance on preventing catastrophic forgetting but also fit the evolving new tasks well.

In this article, considering the supervised dimensionality reduction, we first propose a model, called infinite Bayesian max-margin linear discriminant projection (iMMLDP), by assembling a set of local regions, where we make use of Bayesian nonparametric priors to handle the model selection problem, for example, the underlying number of local regions. In each local region, our model jointly learns a discriminative subspace and the corresponding classifier. Under this framework, iMMLDP combines dimensionality reduction, clustering, and classification in a principled way. Moreover, to deal with more complex data, for example, a local nonlinear separable structure, we extend the linear projection to a nonlinear case based on the kernel trick and develop an infinite kernel max-margin discriminant projection (iKMMDP) model. Thanks to the conjugate property, the parameters in these two models can be inferred efficiently via the Gibbs sampler. Finally, we implement our models on synthesized and real-world data, including multimodally distributed datasets and measured radar image data, to validate their efficiency and effectiveness.

The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by$Q$-learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments.

In this paper, we propose a certificateless noninteractive key exchange protocol. No message exchange is required in the protocol, and this feature will facilitate the applications where the communication overhead matters, for example, the communications between the satellites and the earth. The public key certificate is removed as well as the key escrow problem using the certificateless public key cryptosystem. The security of the protocol rests on the bilinear Diffie–Hellman problem, and it could be proved in the random oracle model. Compared with previous protocols, the new protocol reduces the running time by at least 33.0%.

In this paper, a network intrusion detection system is proposed using Bayesian topic model latent Dirichlet allocation (LDA) for mobile edge computing (MEC). The method employs tcpdump packets and extracts multiple features from the packet headers. The tcpdump packets are transferred into documents based on the features. A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic. Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic. A threshold is defined in the training phase as the minimum likelihood of a host. In the test phase, when a host’s test traffic has a likelihood lower than the host’s threshold, the traffic is labeled as an intrusion. The intrusion detection system is validated using DARPA 1999 dataset. Experiment shows that our method is suitable to protect the security of MEC.

A secure and efficient ID-based registration protocol with user anonymity is proposed in this paper for IP-based mobile networks. The protocol minimizes the registration delay through a minimal usage of the identity (ID)-based signature scheme that eliminates expensive pairing operations. User anonymity is achieved via a temporary identity (TID) transmitted by a mobile user, instead of its true identity. Additional replay protection from a Foreign Agent (FA) is included in the registration messages to prevent a possible replay attack. A formal correctness proof of the protocol using Protocol Composition Logic (PCL) is presented. Numerical analysis and computer simulation results demonstrate that the proposed protocol outperforms the existing ones in terms of the registration delay, the registration signaling traffic, and the computational load on a Mobile Node (MN) while improving security. For example, the proposed protocol reduces the registration delay up to 49.3 percent approximately, comparing to Yang's protocol.