Traffic states typically exhibit regular spatio-temporal patterns that support accurate prediction. However, traffic accidents can disrupt these patterns and introduce irregular fluctuations that challenge forecasting models. Previous studies have focused on predicting accident hotspots, risks, or traffic states using simple accident features. Yet, because traffic accidents are infrequent and exhibit spatio-temporal biases relative to collected traffic sensor data, they may significantly or only slightly affect traffic states, posing challenges for accurate prediction. In this study, we propose a large language model (LLM) enhanced Causality-aware Traffic accidents post-effects prediction (LLeCaT) to uncover the causal relationship between traffic states and accidents. Our model integrates fine-tuned LLMs to extract semantic information from accident records and estimate their effects on future traffic states following their occurrence. To validate the effectiveness of our proposed method, we collected and processed publicly available anonymized traffic accident records, traffic state datasets, and environmental information. We conducted extensive experiments demonstrating its superiority over all baselines. Additional ablation studies and case analyses further confirm the validity of the estimated causal effects.

Blockchain has been regarded as a trusted carrier for distributed data storage. With large volumes of valuable data stored on blockchain, data query has become a major requirement. However, the existing blockchains do not provide efficient query functionality because of their deep-rooted chain structure. Blockchain database is a new direction that constructs index on top of blockchain to provide rich query functionalities. The existing works are either insecure because the query process separates from the blockchain consensus, or inscalable because all the data needs to be stored in the block. In this paper, we propose a novel semantic blockchain database called MSTDB. We design a hybrid on/off chain blockchain storage architecture in which the majority of blockchain storage is offloaded to the off-chain storage and a novel index structure named Merkle Semantic Trie (MST) is designed to be a secure and semantic bridge between on- and off-chain. Based on MST, MSTDB provides a variety of semantic query functions including multi-keyword query, range query, Top-K query, and cross-chain query. To improve the performance further, we design some index compression and query preprocessing techniques for MSTDB. Extensive experiments demonstrate the effectiveness and efficiency of our blockchain database.

Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets—commonly referred to asbig data—and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging ofbig data and emergency management(BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research.

Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the non-independent and identically distributed (Non-IID) data across the clients, which decreases the efficiency of stochastic gradient descent (SGD) based training process. Moreover, clients with different data distributions may cause bias to the global model update, resulting in a degraded model accuracy. To tackle the Non-IID problem in FL, we aim to optimize the local training process and global aggregation simultaneously. For local training, we analyze the effect of hyperparameters (e.g., the batch size, the number of local updates) on the training performance of FL. Guided by the toy example and theoretical analysis, we are motivated to mitigate the negative impacts incurred by Non-IID data via selecting a subset of participants and adaptively adjust their batch size. A deep reinforcement learning based approach has been proposed to adaptively control the training of local models and the phase of global aggregation. Extensive experiments on different datasets show that our method can improve the model accuracy by up to 30 percent, as compared to the state-of-the-art approaches.

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.

Summary The advent of Internet of Things (IoT) paradigm is having a great ripple effect in the field of disaster management as well as many other areas. Victim detection has been studied as rescuer‐ or victim‐oriented approach, which utilises the IoT advanced technologies, but they have weaknesses according to disaster types. In this paper, we propose a Victim Detection Platform (VDP) architecture combining both approaches using various advanced technologies such as IoT, drone and edge/cloud computing to offer higher quality services, which ultimately result in swifter, better responses and more lives saved. First, we have reviewed related literature regarding three crucial issues (ie, multi‐modal evaluation for reliable data, edge‐based real‐time response, and privacy‐preserved Big Data analysis) to satisfy the service. In order to achieve these important aspects, the VDP architecture is described along with roles/relations of technologies and concepts leveraged to and data sources considered in the platform. To explore the realisation possibility of the proposed approach in real disasters, a validation scenario considering the critical issues and details in a victim detection task is illustrated, and then appropriate techniques, which will be utilised in the proposed VDP, and its justifications are discussed. Finally, we debate how the techniques are harmonised in the VDP and what the challenges are.

Modern machine learning (ML) applications are often deployed in the cloud environment to exploit the computational power of clusters. However, this in-cloud computing scheme cannot satisfy the demands of emerging edge intelligence scenarios, including providing personalized models, protecting user privacy, adapting to real-time tasks, and saving resource cost. In order to conquer the limitations of conventional in-cloud computing, there comes the rise of on-device learning, which makes the end-to-end ML procedure totally on user devices, without unnecessary involvement of the cloud. In spite of the promising advantages of on-device learning, implementing a high-performance on-device learning system still faces with many severe challenges, such as insufficient user training data, backward propagation (BP) blocking, and limited peak processing speed. Observing the substantial improvement space in the implementation and acceleration of on-device learning systems, we intend to present a comprehensive analysis of the latest research progress and point out potential optimization directions from the system perspective. This survey presents a software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. We hope this survey could bring fruitful discussions and inspire the researchers to further promote the field of edge intelligence.

Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation algorithms or models therefore do not directly improve the recommendation accuracy. Moreover, extra resources in deriving information are required. Thereby, we propose a collaborative filtering approach using a tensor which is modeled considering 5Ws aspects and generate explanations by combining factorization results with templates. Quality and explanation of recommendations were evaluated on quantitative/qualitative analyses.

The four papers in this special section examine the use of multimedia Big Data for leveraging big events such as natural disasters. Leveraging technological advances and using Big Data approaches for information management and processing in extreme events have attracted much research attention. Multimedia is increasingly becoming the most important and valuable source for insight and information on extreme events. It covers from everyone’s experiences to everything happening in the world. There will be lots of multimedia big data in extreme events—surveillance video, disaster images, social media, voice and video, to name a few, only if their volumes grow to the extent that the traditional multimedia processing and analysis systems may not handle effectively. As such, multimedia big data in extreme events are spurring on tremendous amounts of research and development of related technologies and applications. The papers in this issue is intended to provide a premier forum for researchers working on multimedia big data for extreme events to present their recent research results. It also provides an important opportunity for multidisciplinary studies connecting multimedia computing to disaster information management.

Virtual communities are complex systems interweaving technical and human aspects.We propose here to identify usual functions provided by virtual communities and define a typology based on the common objects or goals shared by their users.The papers included in this special issue show some examples of research works lead in some of these categories of communities.

Web Intelligence deals with intelligent methods and information & communication technologies that are integrated to enhance different web-based applications. Communities are popular, particularly on the World Wide Web, as a means for like-minded