Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs—such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering: as a structured, iterative code-generation process. AEC decomposes ZSEE into specialized subtasks—retrieval, planning, coding, and verification—each handled by a dedicated LLM agent. Event schemas are represented as executable class definitions, enabling deterministic validation and precise feedback via a verification agent. This programming-inspired approach allows for systematic disambiguation and schema enforcement through iterative refinement. By leveraging collaborative agent workflows, AEC enables LLMs to produce precise, complete, and schema-consistent extractions in zero-shot settings. Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines, showcasing the power of treating event extraction like code generation.

Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existing MTPP models rely on channel-mixing strategies that encode information from different event types into a single, fixed-size latent representation. This entanglement can obscure type-specific dynamics, leading to performance degradation and increased risk of overfitting. In this work, we introduce ITPP, a novel channel-independent architecture for MTPP modeling that decouples event type information using an encoder-decoder framework with an ODE-based backbone. Central to ITPP is a type-aware inverted self-attention mechanism, designed to explicitly model inter-channel correlations among heterogeneous event types. This architecture enhances effectiveness and robustness while reducing overfitting. Comprehensive experiments on multiple real-world and synthetic datasets demonstrate that ITPP consistently outperforms state-of-the-art MTPP models in both predictive accuracy and generalization.

Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.

Document retrieval aims to find documents related to the query from all candidate documents. Existing studies develop the Generative Retrieval approach, which assigns a unique DocID to each document, and then measures document-query relevance based on the probability of generating the expected DocID for the given query. However, the generated DocID can have expressive limitation of different semantic topics, leading to semantic gaps in document retrieval. Besides, existing GR models usually suffer from the catastrophic forgetting when memorizing new documents incrementally, which makes real-world application impractical. To overcome these issues, we propose Translational Generative Retrieval, which translates each document into potential queries reflecting different topics, specifically by a sequence of token probability distributions. To better model the semantics of potential queries and effectively decode the target query from the distributions, we propose DirEcted Acyclic Graph Retrieval (DEAR) model, which reforms the distributions from a non-autoregressive generative model into a Directed Acyclic Graph. Experimental results demonstrate that DEAR outperforms existing retrieval models, setting a new state-of-the-art in generative retrieval.

Zero-shot cross-domain slot filling is becoming increasingly important due to its ability to generalize to new domains without the need for annotating domain-specific data, which aligns well with the requirements of industrial deployments. Recent advanced works deal with this task through question answering framework and make remarkable progress. However, they always rely on human efforts to manually construct question templates or prompts for all slot types, which is not only labor consuming, but also experience context inconsistency issue between the manual example and the specific test instance. To alleviate this problem, we introduce a retriever designed to extract reference samples from the training sets, serving as demonstrations to guide the model in generating the target slot entity through in-context learning. Building upon this retriever, we propose a retrieval-augmented generative framework that automatically constructs and tailors prompts to each specific test instance, eliminating the need for manual efforts. Experiment results verify that our approach attains the state-of-the-art.

Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of I) investigating the impact of multi-granular interactions across recent snapshots, and II) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, particularly events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards Historically Relevant Events Structuring (HisRES). Concretely, HisRES comprises two distinctive modules excelling in structuring historically relevant events within TKGs, including a multi-granularity evolutionary encoder that captures structural and temporal dependencies of the most recent snapshots, and a global relevance encoder that concentrates on crucial correlations among events relevant to queries from the entire history. Furthermore, HisRES incorporates a self-gating mechanism for adaptively merging multi-granularity recent and historically relevant structuring representations. Extensive experiments on four event-based benchmarks demonstrate the state-of-the-art performance of HisRES and indicate the superiority and effectiveness of structuring historical relevance for TKG reasoning.

Few-Shot Relation Extraction (FSRE) remains a challenging task due to the scarcity of annotated data and the limited generalization capabilities of existing models. Although large language models (LLMs) have shown potential in FSRE through in-context learning, their general-purpose training objectives often result in suboptimal performance for task-specific relation extraction. To overcome these challenges, we propose TKRE (Two-Stage Knowledge-Guided Pre-training for Relation Extraction), a novel framework that synergistically integrates LLMs with traditional relation extraction models, bridging generative and discriminative learning paradigms. TKRE introduces two key innovations: (1) leveraging LLMs to generate explanation-driven knowledge and schema-constrained synthetic data, addressing the issue of data scarcity; and (2) a two-stage pre-training strategy combining Masked Span Language Modeling (MSLM) and Span-Level Contrastive Learning (SCL) to enhance relational reasoning and generalization. Together, these components enable TKRE to effectively handle FSRE tasks. Comprehensive experiments on benchmark datasets demonstrate the efficacy of TKRE, achieving new state-of-the-art performance in FSRE and underscoring its potential for broader application in low-resource scenarios. The code and data are released on https://github.com/UESTC-GQJ/TKRE.

Understanding and predicting urban dynamics is crucial for managing transportation systems, optimizing urban planning, and enhancing public services. While neural network-based approaches have achieved success, they often rely on task-specific architectures and large volumes of data, limiting their ability to generalize across diverse urban scenarios. Meanwhile, Large Language Models (LLMs) offer strong reasoning and generalization capabilities, yet their application to spatial-temporal urban dynamics remains underexplored. Existing LLM-based methods struggle to effectively integrate multifaceted spatial-temporal data and fail to address distributional shifts between training and testing data, limiting their predictive reliability in real-world applications. To bridge this gap, we propose UrbanMind, a novel spatial-temporal LLM framework for multifaceted urban dynamics prediction that ensures both accurate forecasting and robust generalization. At its core, UrbanMind introduces Muffin-MAE, a multifaceted fusion masked autoencoder with specialized masking strategies that capture intricate spatial-temporal dependencies and intercorrelations among multifaceted urban dynamics. Additionally, we design a semantic-aware prompting and fine-tuning strategy that encodes spatial-temporal contextual details into prompts, enhancing LLMs' ability to reason over spatial-temporal patterns. To further improve generalization, we introduce a test time adaptation mechanism with a test data reconstructor, enabling UrbanMind to dynamically adjust to unseen test data by reconstructing LLM-generated embeddings. Extensive experiments on real-world urban dynamics datasets from multiple cities demonstrate the effectiveness of UrbanMind. The results consistently show that UrbanMind outperforms state-of-the-art baselines, achieving superior accuracy and strong generalization, even in zero-shot scenarios with no prior data.

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model’s ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.

Contextual information parsing is one of the most important subtasks of conversational KBQA. However, existing methods often assume the independence of utterance and model them in isolation. In this paper, we propose a Dependency paRsing‐Enhanced converSational queStion AnswerinG systEm, DRESSAGE, which can effectively model long‐range semantic dependencies in the conversation history. This is a multitask neural semantic parsing model. The model can perform explicit dependency parsing for several history questions and the current question and enhance the entity recognition module and the question encoding module with the parsing tree. The performance of the DRESSAGE model is tested on the widely used CSQA dataset and gets SOTA in the overall effect, which proves the effectiveness of this model.

Stance detection plays an important role in detecting users thoughts and opinions through their comments or feedback. However, few-shot and zero-shot stance detection is not yet ready to analyze a textual document based on various topic aspects. To address the issues, we present a novel sentiment-filtered knowledge-based stance detection based on BERT (SentKB-BERT). By encoding Wikipedia summaries and knowledge graphs between external entities together, the proposed text filtering technique increases the presentation of external knowledge and decreases the lack of sentiment expressions. Additionally, the sentiment and external knowledge are encoded jointly in a unified framework to construct the stance representation. The experimental results demonstrated that our proposed method significantly improves the performance of few-shot and zero-shot stance detection, where the improvements were up to 10.3% in different indicators. The source code is available at https://github.com/FFFcapatin328/SentKB.

Abstract Recent studies have demonstrated that finely tuned deep neural networks (DNNs) are susceptible to adversarial attacks. Conventional physical attacks employ stickers as perturbations, achieving robust adversarial effects but compromising stealthiness. Recent innovations utilise light beams, such as lasers and projectors, for perturbation generation, allowing for stealthy physical attacks at the expense of robustness. In pursuit of implementing both stealthy and robust physical attacks, the authors present an adversarial catoptric light (AdvCL). This method leverages the natural phenomenon of catoptric light to generate perturbations that are both natural and stealthy. AdvCL first formalises the physical parameters of catoptric light and then optimises these parameters using a genetic algorithm to derive the most adversarial perturbation. Finally, the perturbations are deployed in the physical scene to execute stealthy and robust attacks. The proposed method is evaluated across three dimensions: effectiveness, stealthiness, and robustness. Quantitative results obtained in simulated environments demonstrate the efficacy of the proposed method, achieving an attack success rate of 83.5%, surpassing the baseline. The authors utilise common catoptric light as a perturbation to enhance the method's stealthiness, rendering physical samples more natural in appearance. Robustness is affirmed by successfully attacking advanced DNNs with a success rate exceeding 80% in all cases. Additionally, the authors discuss defence strategies against AdvCL and introduce some light‐based physical attacks.

Distributed mobile devices collect unlabeled graph data from environment. Introducing popular graph contrastive learning (GCL) methods can learn node representations better. However, training high-performance GCL requires large-scale data and graph data collected by the single device is insufficient. Meanwhile, transmitting local data for centralized training suffers from non-negotiate privacy leakage and bandwidth consumption. Federated learning (FL), as a distributed learning paradigm, is commonly used for such issues. Nevertheless, direct combination of FL and GCL struggles to supplement global graph information. This absence results in neighbor information missing, thus causing the local GCL to learn biased node representations. Moreover, the combination also triggers potential gradient explosion owing to the lack of unified learning criteria. In this paper, we propose a federal GCL framework that complements missing structural information and provides unified learning criteria. The key idea is to achieve cross-client node alignment on server through local graph structural importance to reason about the global graph information. We design a hierarchical structural importance scoring method to comprehensively evaluate structural importance, thus server performs effective cross-client aggregation while maintaining local graph privacy. We demonstrate the security and prove the bandwidth-reducing advantage of the proposed framework. Extensive experiments on 3 datasets show the superior performance of our method.

As a cost-effective alternative to standard multi-label learning, the multi-label image recognition with partial positive labels (MLR-PPL) task attracts increasing attention, in which merely a portion of positive labels are given while the rest of positive labels and all negative labels are missing. To facilitate this task, we propose a novel framework that leverages semantic correlation among different images in a category-adaptive manner to complement unknown labels accurately. Specifically, the proposed framework consists of two complementary modules. 1) A category-adaptive label discovery (CALD) module is designed to measure the semantic similarity between positive samples and then complement unknown labels with high similarities. 2) A category-adaptive noise rejection (CANR) module is designed to compute the sample weights based on semantic similarities from different samples and discard noisy labels with low weights. Due to the various degrees of confidence calibration among different categories, searching appropriate thresholds for each category in the proposed framework is highly time-consuming. To avoid such a resource-intensive manual tuning, we introduce a category-adaptive threshold updating algorithm that introduces the category-specific positive and negative similarity to adjust the threshold adaptively. Extensive experiments on various benchmarks show that the proposed framework performs better than current state-of-the-art algorithms.

Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizability. One promising but underexplored solution is to compensate for the novel classes by generating plausible samples. However, most existing works of this line exploit visual information only, rendering the generated data easy to be distracted by some challenging factors contained in the few available samples. Being aware of the semantic information in the textual modality that reflects human concepts, this work proposes a novel framework that exploits semantic relations to guide dual-view data hallucination for few-shot image recognition. The proposed framework enables generating more diverse and reasonable data samples for novel classes through effective information transfer from base classes. Specifically, an instance-view data hallucination module hallucinates each sample of a novel class to generate new data by employing local semantic correlated attention and global semantic feature fusion derived from base classes. Meanwhile, a prototype-view data hallucination module exploits semantic-aware measure to estimate the prototype of a novel class and the associated distribution from the few samples, which thereby harvests the prototype as a more stable sample and enables resampling a large number of samples. We conduct extensive experiments and comparisons with state-of-the-art methods on several popular few-shot benchmarks to verify the effectiveness of the proposed framework.

Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional information of the news, which could be beneficial since it contains the subjective intentions of the authors; (2) pay little attention to the relation (similarity) between the title and textual information in news articles, which often use irrelevant title to attract reader' attention. To this end, we propose a novel Title-Text similarity and emotion-aware Fake news detection (TieFake) method by jointly modeling the multi-modal context information and the author sentiment in a unified framework. Specifically, we respectively employ BERT and ResNeSt to learn the representations for text and images, and utilize publisher emotion extractor to capture the author's subjective emotion in the news content. We also propose a scale-dot product attention mechanism to capture the similarity between title features and textual features. Experiments are conducted on two publicly available multi-modal datasets, and the results demonstrate that our proposed method can significantly improve the performance of fake news detection. Our code is available at https://github.com/UESTC-GQJ/TieFake.

The accurate and comprehensive fault diagnosis is pivotal for the maintenance of mechanical equipment, which may directly impact the safety in production and avoid unnecessary losses of equipment. With the pervasively adopted sensing components, sensing data can be collected from mechanical equipment and analyzed for fault diagnosis. Previous solutions apply sophisticated deep learning models for such fault diagnosis tasks and have achieved impressive performance. However, these methods usually ignore the existences of diverse characteristics of faults, and formulate the fault diagnosis as a simple classification problem. Considering necessity of comprehensive and multi-view fault diagnosis, this work proposes a novel dual-view learning framework for combinatorial mechanical fault classification. The framework includes dual encoders to simultaneously learn the feature representation tied with the position and size of faults on equipment. Then these features are mutually mixed for better representation and the mixed features are merged and feed into deep neural networks for further representation learning. The query samples are classified through metric-based comparison to search for the closest type of composite fault. Moreover, the proposed framework also applies typical metric-based meta learning method to handle the issue of small training datasets, and adopts semi-supervised learning method to make use of both labeled and unlabeled samples. Finally, the experimental results on public datasets show that the proposed method can outperform previous solutions and achieve state-of-the-art performance.

Knowledge Graphs(KGs) have been incorporated as external information into recommendation systems to ensure the high-confidence system. Recently, Contrastive Learning(CL) framework has been widely used in knowledge-based recommendation, owing to the ability to mitigate data sparsity and it considers the expandable computing of the system. However, existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge: (1) For the knowledge view generation, they only perform simple data augmentation operations on KGs, resulting in the introduction of noise and irrelevant information, and the loss of essential information. (2)For the knowledge view encoder, they simply add the edge information into some GNN models, without considering the relations between edges and entities. Therefore, this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL) framework, which generates dual views from user-item interaction graph and KG. Specifically, through data enhancement technology, KRCL introduces historical interaction information, background knowledge and item-item semantic information. Then, a novel relation-aware GNN model is proposed to encode the knowledge view. Finally, through the designed contrastive loss, the representations of the same item in different views are closer to each other. Compared with various recommendation methods on benchmark datasets , KRCL has shown significant improvement in different scenarios.

Integrating massive social data with traditional social sciences, the computational social science (CSS) is crucial for understanding the Internet of Things (IoT)-based nondeterministic social systems. Event predictability is the fundamental premise of widespread societal event predictions with CSS. Due to data quality, model suitability, and the nondeterministic nature of IoT-based social systems, the event predictability is difficult to be characterized. Based on Turing computability and prediction error tolerability, this article posits a uniform event predictability theory. With discrepancy and Rademacher complexity, the generalization error bound is utilized to represent data quality and model suitability. Together with thresholds for the generalization error bound and confidence, the event predictability is modeled in a probabilistic manner to capture the nondeterminism within IoT-based social systems. The event predictability theory is theoretically proved and validated, and utilizing the proposed approximation algorithm for discriminating event predictability (AADEP), its applicability is further verified by experiments on a real-world data set for IoT-based nondeterministic social systems.

Principal Component Analysis (PCA) has been widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA), under different robust distance metrics, such as ℓ 1 -norm and ℓ 2 , p -norm, can deal with noise or outliers to some extent. However, real-world data may display structures that can not be fully captured by these simple functions. In addition, existing methods treat complex and simple samples equally. By contrast, a learning pattern typically adopted by human beings is to learn from simple to complex and less to more. Based on this principle, we propose a novel method called Self-paced PCA (SPCA) to further reduce the effect of noise and outliers. Notably, the complexity of each sample is calculated at the beginning of each iteration in order to integrate samples from simple to more complex into training. Based on an alternating optimization, SPCA finds an optimal projection matrix and filters out outliers iteratively. Theoretical analysis is presented to show the rationality of SPCA. Extensive experiments on popular data sets demonstrate that the proposed method can improve the state-of-the-art results considerably.

The easy and pervasive involvement of devices in Industrial Internet of Things has greatly benefited the implementation and adoption of various smart services. One prominent prerequisite of such trends is the extensive and continuous support and sharing of data and resources among devices. However, previous efforts usually treat the data sharing as one-time task among devices, which are incapable when the data are applied for the distributed and iterative training task of machine learning models. Therefore, this article proposes a novel framework for continuous data sharing in Industrial Internet of Things. The system consists of different system owners, each brings devices and participate the distributed training of models. Specifically, system owners hold different scales of devices, data, and resources, while devices own heterogeneous availability in different time periods. In this case, the goal is to properly assign devices for qualified model training process in different rounds, such that no devices will devote unlimited resources and the overall efforts and consumptions among different owners are balanced. Accordingly, three algorithms for device allocation are proposed, based on whether the availability of devices in each training round are known at the beginning of the training procedure. The analysis shows that all algorithms can achieve a rational allocation for devices and balance the performance among system owners. Finally, evaluation results reveal that the proposed solutions outperform baseline methods in providing better data sharing plans.

Multi-view graph clustering has been intensively investigated during the past years. However, existing methods are still limited in two main aspects. On the one hand, most of them can not deal with data that have both attributes and graphs. Nowadays, multi-view attributed graph data are ubiquitous and the need for effective clustering methods is growing. On the other hand, many state-of-the-art algorithms are either shallow or deep models. Shallow methods may seriously restrict their capacity for modeling complex data, while deep approaches often involve large number of parameters and are expensive to train in terms of running time and space needed. In this paper, we propose a novel multi-view attributed graph clustering (MAGC) framework, which exploits both node attributes and graphs. Our novelty lies in three aspects. First, instead of deep neural networks, we apply a graph filtering technique to achieve a smooth node representation. Second, the original graph could be noisy or incomplete and is not directly applicable, thus we learn a consensus graph from data by considering the heterogeneous views. Third, high-order relations are explored in a flexible way by designing a new regularizer. Extensive experiments demonstrate the superiority of our method in terms of effectiveness and efficiency.

Graph clustering is a prevalent issue associated with social networks, data mining, and machine learning; its objective is to detect communities or groups in networks. Inspired by the recent success of deep learning (DL), new DL-based graph clustering methods have achieved promising results. However, a deep neural network involves a large number of training parameters. Moreover, existing methods typically select the similarity metric by an ad hoc approach, which considerably affects the resulting output. In this study, we propose a principled graph learning perspective, fine-grained attributed graph clustering. Based on a shallow approach, the proposed method sufficiently exploits both node features and structure information by benefiting from graph convolution. Consequently, a fine-grained graph encoded higher-order relations is automatically learned. Comprehensive experiments on benchmark datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms, including several DL methods.

Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge volumes of graph data generated in IoT. These data are believed to brought significant benefits to both the operation and functionalities of IoT systems, especially when combined with cutting-edge Artificial Intelligence techniques. However, these graph data are usually locally collected by data contributors with sensing devices, which could be both partially overlapped as they record same environment, and sensitive as they can indicate private physical status of contributors. Considering all challenges, current solutions for graph data collection in IoT are incapable. Therefore, this article proposes a novel framework for privacy-preserving distributed graph data collection for IoT. The framework allows the graphs kept by data contributors to be partially overlapped, and can help the data broker to efficiently derive the universal view by combining these graphs. The differential privacy is applied for privacy preservation during data collection. The proposed problem aims at minimizing the total bandwidth consumption for graph collection, which is proved to be NP-complete. Then three algorithms are proposed for different circumstances, based on the diverse knowledge and purposes held by the data broker. Finally, both theoretical and numerical analysis have demonstrated the advancement of these methods.

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

Attempting to fully exploit the rich information of topological structure and node features for attributed graph, we introduce self-supervised learning mechanism to graph representation learning and propose a novel Self-supervised Consensus Representation Learning (SCRL) framework. In contrast to most existing works that only explore one graph, our proposed SCRL method treats graph from two perspectives: topology graph and feature graph. We argue that their embeddings should share some common information, which could serve as a supervisory signal. Specifically, we construct the feature graph of node features via k-nearest neighbour algorithm. Then graph convolutional network (GCN) encoders extract features from two graphs respectively. Self-supervised loss is designed to maximize the agreement of the embeddings of the same node in the topology graph and the feature graph. Extensive experiments on real citation networks and social networks demonstrate the superiority of our proposed SCRL over the state-of-the-art methods on semi-supervised node classification task. Meanwhile, compared with its main competitors, SCRL is rather efficient.

Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. Existing methods of PPI network alignment often try to improve the coverage ratio of the alignment result by aligning all proteins from different species. However, there is a fundamental biological premise that needs to be considered carefully: not every protein in a species can, nor should, find its homologous proteins in other species. In this work, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we apply a representation learning method on the networks and generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against previous multiple alignment methods.

The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains. It has been treated as one prominent prerequisite for enabling AI techniques in wireless networks. With the emergence of diversified smart devices and applications, data held by individuals becomes more pervasive and nontrivial for publication. First, the data are more private and sensitive, as they cover every aspect of daily life, from the incoming data to the fitness data. Second, the publication of such data is also bandwidth‐consuming, as they are likely to be stored on mobile devices. The local differential privacy has been considered a novel paradigm for such distributed data publication. However, existing works mostly request the encoding of contents into vector space for publication, which is still costly in network resources. Therefore, this work proposes a novel framework for highly efficient privacy‐preserving data publication. Specifically, two sampling‐based algorithms are proposed for the histogram publication, which is an important statistic for data analysis. The first algorithm applies a bit‐level sampling strategy to both reduce the overall bandwidth and balance the cost among contributors. The second algorithm allows consumers to adjust their focus on different intervals and can properly allocate the sampling ratios to optimize the overall performance. Both the analysis and the validation of real‐world data traces have demonstrated the advancement of our work.

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real‐time trajectory data and requests stronger spatial‐temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real‐time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real‐world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real‐world data traces, and the results validate the improvement of proposed algorithms.

To identify and explore the similarities between diseases is of great significance for u nderstanding t he pathogenic mechanisms of emerging complex diseases. Some methods try to measure the similarity of diseases through deep learning models. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to retrieve similar diseases by few-shot learning. To deal with the problem of insufficient number o f labelled similar disease pairs, we design double tasks to optimize the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results illustrate the overall effectiveness by comparing with prior methods on few labeled training dataset.

In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%.

Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the data can be separated into different subspaces. However, this simple assumption does not always hold since the raw data might not be separable into subspaces. To recover the "clustering-friendly" representation and facilitate the subsequent clustering, we propose a graph filtering approach by which a smooth representation is achieved. Specifically, it injects graph similarity into data features by applying a low-pass filter to extract useful data representations for clustering. Extensive experiments on image and document clustering datasets demonstrate that our method improves upon state-of-the-art subspace clustering techniques. Especially, its comparable performance with deep learning methods emphasizes the effectiveness of the simple graph filtering scheme for many real-world applications. An ablation study shows that graph filtering can remove noise, preserve structure in the image, and increase the separability of classes.

The collection of high-confidence data has been one prominent step for many services in smart city systems. However, the privacy issues have been thwarting the seamless publication of data, especially as the data from different aspects of daily life may provide unprecedented coverage of contributors. Current solutions have been carefully designed to perturb or suppress the data before publication, so as to balance the privacy and utilities. However, they cannot fit the practice in smart cities, where multiple service providers request information on heterogeneous domains and regions of the city. Therefore, this article proposes a novel framework for data publication of workers in smart city systems. The framework allows workers and requestors to own and request various types of contents in different regions. The objective is to maximize the number of service providers receiving qualified utilities under privacy constraints. Furthermore, differential privacy is applied to guarantee that workers will not disclose personal information to requestors. In the technical part, the problem is proved to be NP-complete. Then two algorithms and strategies are proposed toward different cases: 1) workers apply identical privacy budgets for all published data and 2) workers are flexible on privacy settings. Both algorithms are theoretically analyzed on their performance of the released results. Finally, the evaluation of data sets of local businesses reveals that proposed algorithms can outperform baseline methods.

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

Quantifying the similarities between diseases is now playing an important role in biology and medicine, which provides reliable reference information in finding similar diseases. Most of the previous methods for similarity calculation between diseases either use a single-source data or do not fully utilize multi-sources data. In this study, we propose an approach to measure disease similarity by utilizing multiple heterogeneous disease information networks. Firstly, multiple disease-related data sources are formulated as heterogeneous disease information networks which include various types of objects such as disease, pathway, and chemicals. Then, the corresponding subgraphs of these heterogeneous disease information networks are obtained by filtering vertices. Topological scores and semantics scores are calculated in these heterogenous subgraphs using Dynamic Time Warping (DTW) algorithm and meta path method respectively. In this way, we transform multiple heterogeneous disease networks to a homogeneous disease network with different weights on the edges. Finally, the disease nodes can be embedded according to the weights and the similarity between diseases can then be calculated using these n-dimensional vectors. Experiments based on benchmark set fully demonstrate the effectiveness of our method in measuring the similarity of diseases through multi-sources data.

With the technology of Network Function Virtualization (NFV), a multicast service (e.g., real-time multimedia streaming or event monitoring) may be accommodated with a Service Function Chain (SFC). The SFC consists of an ordered set of network functions running on generic physical hardware to provide services from source node to each destination node of the multicast service. In this paper, we define the problem of Multicast-aware Service Function Tree Embedding (M-SFTE), which allows the multicast flows to traverse through network service functions before reaching destination nodes. The M-SFTE maps user's SFC-based multicast requests onto a shared substrate network while considering the constraints of network functionality, computing demand of each virtual network function node, and the bandwidth demand of the request. We propose a novel algorithm, called Minimum Cost Multicast Service Function Tree (MC-MSFT) to jointly optimize the process of constructing SFC-based multicast tree and allocating requested resource to embed the tree onto the physical network. The experimental results show that the MC-MSFT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.

Network Function Virtualization (NFV) takes advantages of the emerging technologies in virtualization and automation to offer new ways in design, deployment, and management of networking services. In NFV, the proprietary hardware-based network functions are replaced by the software-based modules named as Virtual Network Functions (VNFs) or Service Functions (SFs). A network service request from the customer can be formed by multiple SFs. To satisfy a network service request, the service provider has to chain the SFs in the request into a Service Function Chain (SFC) and embed the constructed SFC onto the shared substrate network. In this paper, we comprehensively study how to composite and embed an SFC onto a shared substrate network with unique service function. We formulate this problem with the Integer Linear Programming (ILP) technique. We also propose an efficient heuristic algorithm with 2-approximation boundary, namely, Spanning Closed Walk based SFC Embedding (SCW-SFCE). Our extensive simulations and analysis show that the proposed approach can achieve near-optimal performance in a small network and outperform the Nearest Neighbour (NN) algorithm.

Network Function Virtualization (NFV) technology decouples network functions from proprietary hardware equipments. As a result, Internet Service Providers (ISPs) implement software-based network functions on generic highvolume substrate network devices. In NFV, a Service Function Chain (SFC) is defined as an ordered set of abstract network functions running on specific substrate nodes (e.g., servers). A challenging issue in NFV management and orchestration is how to optimize the Dependence-aware SFC Embedding in substrate Optical networks (D_SFCE_O). In this paper, we propose a novel algorithm, namely, Dependence-aware SFC embedding with Least-Used consecutive subcarriers (D_SFC_LU), which jointly optimizes SFC design, SFC mapping and spectrum allocation in optical networks. To minimize resource consumption, D_SFC_LU takes advantages of the proposed techniques: Impact Factor based Node Selection (IFNS), Chain Node Mapping (CNM) and Chain-Fit (CF) spectrum allocation. Our simulation and analysis demonstrate that D_SFC_LU can efficiently embed a network requests while minimizing the required substrate resource in optical networks.

In Network Function Virtualization (NFV), Service Function Chain (SFC) is composed of Virtual Network Function (VNF) nodes that are chained via VNF links. SFCs can be specified as unidirectional or bidirectional. A unidirectional SFC (u-SFC) demands the traffic being forwarded via the VNFs in one direction, while a bidirectional SFC (b-SFC) requires bidirectional traffic flows. In this paper, for the first time, we investigate the problem of how to efficiently deploy a hybrid SFC (h-SFC), whereas some VNF nodes are required to process bidirectional traffic while others only handle unidirectional traffic. We define a new problem called hybrid SFC Deployment (h-SFCD). When each substrate node provides one unique VNF, we prove the NP-hardness of the h-SFCD problem and propose an approximate algorithm, namely, 2-approximation Hybrid Service function chain Deployment in Unique function networks (2-HSD-U). Our experimental results show that the proposed 2-HSD-U algorithm significantly outperforms the heuristic algorithm based on the traditional Nearest-Neighbor technique.

In the age of emerging applications, such as Internet of Things (IoT), big data, and data mining, our life becomes more convenient through customized services that utilize a huge amount of personal data generated and collected by various IoT devices. To fully exploit the data value as well as enhance the data utilization, more and more data are being traded in online data markets. While enjoying the benefit from data trading, data sellers are also suffering from severe risk of privacy leakage. In this paper, our objective is to maximize data seller's received utility via balancing the tradeoff between data trading benefit and data privacy cost. To achieve this, contract theory is utilized to design optimal contract trading mechanisms for both complete and incomplete information markets. From our thorough theoretical analysis, comprehensive simulations, and real-data experiments, the effectiveness of our proposed optimal contract mechanisms can be validated, i.e., the maximum utility can be obtained at the seller side, the individual rationality and incentive compatibility can be guaranteed at the buyer side, and the advantages of our mechanism over the single contract mechanisms can be confirmed.

With dramatic developments of blockchain technology, a number of blockchain-based applications emerge rapidly, among which the incorporation of blockchain into Internet of Things is one of the most valued research direction. Such powerful incorporation is a double-sided sword, i.e., it can benefit both individuals and society but has the vulnerability to coin hopping attack that is a new type of pool mining attack and hard to happen in traditional blockchain networks. In this paper, we theoretically prove the feasibility of coin hopping attack, deeply analyze the conditions of attack implementation, and comprehensively investigate the impacts of coin hopping attack. Moreover, some defense strategies are addressed. To our best knowledge, this paper is the first work targeting coin hopping attack.

In social networks, several influential individuals can promote an idea or a product to numerous individuals. Thus, it is valuable to solve the influence maximization (IM) problem, which asks for finding the most influential set of individuals in a social network. To estimate the influence of individuals, the existing independent cascade (IC) model simulates the influence diffusion only considering the influences from direct in-neighbors to nodes. This consideration does not hold in real life. In many cases, people are likely influenced by information depending on where it comes from, instead of who gives it. To simulate the influence diffusion more accurate, this paper proposes the second-order IC model, which takes the previous influence into consideration. In addition, we design an approximate algorithm and its distributed extension for IM under the second-order IC model. Experimental results show that our second-order IC model outperforms the IC model in terms of simulating influence diffusions. The proposed algorithms are efficient, and the obtained node sets are influential.

Subgraph matching query is to find out the sub-graphs of data graph G which match a given query graph Q. Traditional methods can not deal with big data graphs due to their high computational complex. In this paper, we propose a distributed top-k subgraph search method over big graphs. The proposed method is designed at the level of single vertex and all vertices obtain their matching state separately without requiring global graph information. Therefore, it can be easily deployed in distributed platform like Hadoop. The evaluations of running time, number of messages and supersteps show the efficiency and scalability of the proposed method.

Network virtualization in optical networks enables the decoupling of network services from the underlying hardware infrastructure to allow multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The challenge of mapping VORs onto the shared SON lies on how to efficiently allocate physical resource for the VORs, which is referred to as Virtual Optical Network Embedding (VONE). Many recent research focus on the NP-Hard VONE optimization problem. In this paper, for the first time, we explore how to efficiently map a given VOR for a multicast service onto a shared SON while considering the fanout (splitting/forwarding) limitation of the physical optical switches. We propose a novel algorithm, namely, Centrality-based Degree Bounded Shortest Path Tree (C-DB-SPT) to minimize the resource usage while satisfying the degree limitation in the shared SON. The experimental results show that the C-DB-SPT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.

Transforming infrastructures, buildings and services with the sensed data from the Internet of Things (IoT) technique has drawn wide attention. Enormous video data from city surveillance cameras poses huge challenges of transmission, storage and analysis, which necessitates new video compression technologies. The fusion of video data generated from smart city could be used to support city management and urban policy. Based on the specific characteristics of surveillance video, which are successive pictures have very strong correlations and each picture can be divided into background and foreground, this work proposes a block-level background modeling (BBM) algorithm to support long-term reference structure for efficient surveillance video coding. A rate–distortion optimization for surveillance source (SRDO) algorithm is also developed to improve the coding performance. Experimental results show that the proposed BBM and SRDO can significantly improve the compression performance, which can effectively support diverse video applications in smart city.

Modern applications and services leveraged by interactive cyberphysical systems (CPS) are providing significant convenience to our daily life in various aspects at present. Clients submit their requests including query contents to CPS servers to enjoy diverse services such as health care, automatic driving, and location-based services. However, privacy concerns arise at the same time. Content privacy is recognized and a lot of efforts have been made in the literature of privacy preserving in interactive cyberphysical systems such as location-based services. Nevertheless, neither the cloaking based solutions nor existing client based solutions have achieved effective content privacy by optimizing proper content privacy metrics. In this paper we formulate the problem of achieving the optimal content privacy in interactive cyberphysical systems using k -anonymity solutions based on two content privacy metrics, which are defined using the concepts of entropy and differential privacy. Then we propose an algorithm, Multilayer Alignment (MLA), to establish k -anonymity mechanisms for preserving content privacy in interactive cyberphysical systems. Our proposed MLA is theoretically proved to achieve the optimal content privacy in terms of both the entropy based and the differential privacy mannered content privacy metrics. Evaluation based on real-life datasets is conducted, and the evaluation results validate the effectiveness of our proposed algorithm.

With network virtualization over Elastic Optical Networks (EONs), network services are decoupled from the underlying hardware infrastructure to enable multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The embedding process of VORs onto the shared SON while satisfying the computing resource and spectrum allocation constraints is referred to Virtual Optical Network Embedding (VONE), which is an NP-Hard problem. In this paper, for the first time, we investigate how to efficiently map a given VOR in the form of virtual optical multicast tree onto an SON. We propose a novel algorithm that is called Impact Factor based Virtual Optical Multicast Tree Embedding (IF-VOMTE) to minimize the resource usage and avoid redundant multicast transmission in the shared SON. The experimental results show that our algorithm outperforms the schemes based on traditional techniques such as Greedy Node Mapping (GNM-SP) and First-Fit Node Mapping (FFNM-SP) in terms of the total cost of bandwidth consumption and the reduction of redundant multicast transmission.

The emerging Network Function Virtualization (NFV) technology decouples network functions from the proprietary hardware, which allows the Internet Service Providers (ISPs) to implement network functions as software running on top of a physical (or substrate) node. With NFV, a Service Function Chain (SFC) is defined as an ordered set of network function instances running on specific substrate network nodes to provide services for client users. In this paper, we define the problem of Dependence- Aware Service Function Chain (D_SFC) design and mapping. We study how to efficiently accommodate user's D_SFC requests in the substrate network while considering the constraints of function dependence, computing demand of virtual nodes and bandwidth demand of the D_SFC. We propose a novel heuristic algorithm, called D_SFC design and resource allocation with Adaptive Mapping (D_SFC_AM), which jointly optimizes the processes of designing a D_SFC and allocating resources requested by the chain. D_SFC_AM employs the proposed techniques of dependence sorting and independent grouping that effectively take into account the node dependence and the resource status of the substrate network. Our experimental results show that the proposed algorithm significantly outperforms the scheme based on the traditional topological sorting in which the process of designing a D_SFC and allocating resources requested by the chain is done sequentially.

Network virtualization enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate/physical Network (SN) shared by multiple Virtual Network (VN) requests. The process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to Virtual Network Embedding (VNE) as an NP-hard problem. In this paper, for the first time, we explore how to efficiently map a given Virtual Multicast Tree (VMT) request onto a substrate network. We propose a novel algorithm, namely, Virtual Multicast Tree Embedding based on dynamic Impact Factor (VMTE-IF) to minimize the required resource and redundant multicast transmission in the substrate network. The experimental results show that our algorithm outperforms the traditional greedy-based algorithms over 50% in terms of the cost of bandwidth consumption.

The quantization parameter (QP) value and Lagrangian multiplier (λ) are the key factors for an encoder to achieve the trade-off between visual quality and bit-rate in next generation multimedia communications. In this work, we propose a novel temporal redundancy ratio (TRR) model to determinate hierarchical QPs. Taking the temporal redundancy information, intensity and fluctuation into consideration, the TRR model is constructed as the ratio of mean and variance from the temporal redundancy, and then used to allocate a proper QP value for an individual picture to achieve bit-rate saving and improve the visual quality. We implement the TRR model based QP determination scheme into the reference software HM16.7. Under the common test condition (CTC), simulation results show that the proposed TRR model obtains 1.28% and 1.52% BD-Rate gain over HM16.7 for Low-Delay and Random-Access in 8-bit video coding respectively. Meanwhile, more BD-Rate gains are obtained in 10-bit coding and the screen coding class.

We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.

Social networks such as Twitter and Facebook have become important sources for users to acquire information. In those social networks, users obtain information from the posts/reposts of their social connections. To acquire information efficiently, users are motivated to connect to users who offer attractive and timely information. In this paper, we study how to effectively optimize social connections to optimize the efficiency of information Acquisition. We define this as the problem of Social Connection Optimization for efficient Information Acquisition (SCOIA). We present our analysis on the information accuracy and timeliness to measure the efficiency of information acquisition. Based on the analysis, a novel User Set Selection (USS) algorithm is then proposed to efficiently solve the SCOIA problem. Our simulations based on the crawled Twitter dataset show that the proposed algorithm can efficiently identify user connections, leading to high information acquisition accuracy, low spam rate and low information acquisition latency.

Due to the high performance in the rate control of MPEG-4, the quadratic Rate-Distortion model has been widely proved. The rate control module in the reference software of H.264/AVC directly inherits the quadratic model. However, the introduction of Rate Distortion Optimization (RDO) in H.264/AVC makes rate control more complex than previous standards. Based on theoretical derivation and extensive experiments, this paper analyzes the model error of the quadratic model and the prediction error of MAD in H.264/AVC. Simulation results demonstrate that our proposed linear MAD model achieves higher prediction accuracy than the quadratic mode does.

Rate control in H.264/AVC aims to achieve the best tradeoff between encoding quality and bandwidth while satisfying the buffer restriction. Due to the improving efficiency of intra-only rate control, we propose an effective rate control scheme for intra-only encoding. The proposed scheme employs a novel rate-distortion (RD) model, a new complexity measure, a precise quantization parameter (QP) calculation method, and a simple but effective model adaptation mechanism for intra-frames. Experimental results demonstrate that, compared with JVT-W042, the proposed algorithm achieves higher precise bit estimation, provides more robust buffer control, and improves coding quality.

Rate control regulates the output bit rate of a video encoder in order to obtain optimum visual quality within the available network bandwidth and to maintain buffer fullness within a specified tolerance range. In this paper, we propose a novel rate control scheme for H.264/AVC video compression with a number of new features. We first introduce a calculation approach of frame complexity based on the linear prediction theory. Then, we propose a joint rate-distortion model which is an integration of a liner rate-complexity model and an exponential rate-quantization model. Finally, we develop an effective target bit estimation approach. Experimental results show that, compared with JVT-W042, our scheme achieves more accurate rate regulation, provides robust buffer control, efficiently reduces frame skipping, and improves visual quality.

Rate control plays a crucial role for video communication applications. It ensures that the generated compressed bit streams satisfy bandwidth and buffer constraints. Rate control algorithms recommended by H.264/AVC adopt rate-distortion (R-D) models for inter-frames to determine quantization parameters (QPs) but not for intra-frames. Instead, they directly compute QPs without any considerations of bitrates and coding complexities for intra-frames. In order to obtain more accurate target bit prediction for intra-frames, we first introduce the geometry gradient information as a new complexity measure to accurately represent the complexities for intra-frames. Then, we propose a novel R-D model which is an integration of a linear rate-complexity model and an exponential rate-quantization model. Finally, we develop an accurate and robust intra-only rate control algorithm for H.264/AVC. Experimental results demonstrate that, compared with JVT-W042, the proposed algorithm achieves higher precise bit estimation, provides more robust buffer control, and also improves coding quality.