Inverse rendering is widely used in digital twins, virtual asset generation, and VR/AR. When reconstructing scene illumination, predominant methods rely on environment maps, which simplify illumination into a distant uniform source, struggling to accurately model spatially varying effects. Inspired by the rapid development and representational power of 3D Gaussian Splatting (3DGS), we propose GS-SVIR, a novel inverse rendering framework that leverages 3DGS for the modeling of spatially varying illumination. The proposed framework is structured around three main parts. Firstly, we introduce a novel lighting representation termed ”Gaussian-emitter,” which models the scene illumination as a collection of 3D Gaussians in space, thereby effectively overcoming the limitation of environment maps in modeling spatially varying and near-field lighting. Secondly, to enable high-fidelity rendering under this lighting model, we employ a differentiable ray tracer that calculates direct illumination from the Gaussian emitters. Thirdly, we account for global illumination by approximating multiple-bounce indirect lighting using spherical harmonics coefficients stored within each Gaussian, which significantly enhances the realism of the reconstructed materials and lighting. Experimental results on benchmark datasets demonstrate that our method achieves an average improvement of 4% in material reconstruction accuracy and 3% in rendering quality compared to environment-map-based methods.

In large-scale urban areas, edge computing, with flexible and low-latency services, enriches various pioneering applications such as the internet of vehicles (IoVs) and smart cities. These location-sensitive applications raise a critical concern, i.e., the mismatch between the spatial distributions of computing requirements and computing capacities. And this mismatch gives a huge challenge for load balancing among edge servers. However, existing approaches do not account for this spatial unevenness, which undermines the high-quality implementations of urban edge computing systems. Regarding this load balancing problem, we propose a novel Power Diagram based Edge Balancing (PDEB) approach, pursuing computing capacities self-adapted with computing requirements via the power diagram. This paper makes three key contributions. First, we propose a distribution modeling framework that formulates spatial mismatch as an optimization problem. Second, we introduce a decentralized power diagram constructed from modeled distributions, serving as the mathematical foundation for decentralized edge coordination. Third, we develop a structured pairing and scheduling strategy based on the power diagram to proactively redistribute load across edge servers. Experimentally, PDEB achieves$42.78\%$better load balance and$16.69\%$lower queuing delay than leading baselines, validating its theoretical and practical advantages.

Automated vector floorplan generation is valuable for designers to explore potential spatial designs. However, existing learning-based methods rely on complex post-processing or optimization to obtain plausible vector floorplans, which disrupts the end-to-end design flow. In this paper, we propose FloorplanSBS, a patch-based segmentation framework for directly synthesizing vector floorplans. Our method leverages the strengths of box-based representation and segmentation-based generation, following a division-and-labeling scheme. The framework operates in two stages: given input design constraints, a division model first divides the design space into rectangular patches, followed by a labelling model that assigns semantic labels to each patch. FloorplanSBS supports constraints such as boundaries and layout graphs. Extensive evaluations show that it surpasses state-of-the-art methods in generating high-quality vector floorplans. With its end-to-end neural framework, FloorplanSBS eliminates the need for post-processing, offering a simple, efficient, and user-friendly tool for vector floorplan design.

Abstract Point cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high‐frequency variation remains a major bottleneck; existing learning‐based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency‐aware hierarchical network that precisely tackles those challenges. Our Frequency‐Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross‐scale cues, ensuring that both fine‐grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency‐Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over‐smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real‐world scans (SceneNN) demonstrate that FAHNet outperforms state‐of‐the‐art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.

With the rapid development of Mobile Edge Computing (MEC), various heterogeneous applications have being deployed on edge servers in close proximity to end-users for the low-latency responses. In this circumstance, since the resources on edge servers are limited, it is critical to deploy these applications on suitable edge servers. However, due to the heterogeneity of the applications and the mobility of end-users in real MEC circumstances, the requests each edge server received may undergo temporal fluctuations in both views of quantity and type. In other words, it is crucial to re-deploy these heterogeneous applications to match these dynamic circumstances, instead of permanent deployments without adjustments. Nevertheless, frequent re-deployment causes service interruptions and resource wastage, leading to system instability. Existing approaches struggle to handle redeployment effectively in heterogeneous, dynamic, and stability-critical MEC environments. In this paper, we first formulate the Edge Application Re-Deployment problem on the basis of constrained multi-objective optimization and prove its$\mathcal {NP}$-hardness. Then we propose an optimal re-deployment approach based on the Integer Programming technique for small-scale edge application re-deployment scenarios. And we also propose a Decompose-Solve-Merge approximation approach which balances the effectiveness and efficiency with a configurable parameter for large-scale scenarios. Extensive experiments on a real-world data set evaluate our novel approaches against four existing representative approaches. Additionally, we perform the ablation experiment to validate the effectiveness of our approaches and explore the impact of configurable parameter on the performance. The results show the superior performance of our approaches on re-deployment in terms of heterogeneous, dynamic, and stability.

The existing selfish node detection algorithms for opportunistic networks do not consider the impact of node energy and cache on node selfishness, resulting in inaccuracies and high latency in the detection algorithm. Therefore, this paper proposes a selfish node detection algorithm based on credibility combined with energy and cache (CDEC). Firstly, a new credibility model is presented. Then, through an analysis of the residual energy and cache usage ratio of nodes over time, selfish nodes have greater differences compared to cooperative nodes. Finally, the overall service quality of nodes is combined with their credibility, residual energy, and cache usage ratio to detect selfish nodes based on their service quality. Simulation results show that compared with existing Watchdog and 2-ACK detection algorithms, the CDEC algorithm can effectively improve the accuracy of selfish node detection and message delivery rates while reducing network latency.

The existing opportunistic network routing algorithms assume that nodes do not exhibit selfish behavior. In a well-cooperating network, these algorithms can effectively deliver messages. However, when nodes behave selfishly, the efficiency of these algorithms decreases. To address this issue, this paper proposes a Filtering Algorithm of Selfish Node(FASN).This algorithm filters selfish nodes by reducing the transmission probability of selfish nodes. Additionally, to alleviate network congestion, a new ACK confirmation mechanism is proposed. This mechanism not only removes redundant messages that have already been delivered but also deletes messages with a survival rate of 0. Finally, node differentiation punishment is achieved based on the number of times a node is added to a blacklist. Experimental results demonstrate that the FASN algorithm is suitable for opportunistic networks with high security requirements. Moreover, it effectively filters out selfish nodes and outperforms the CDEC algorithm and several classical routing algorithms in terms of delivery rate, overhead, and average delay.

The movement of nodes in opportunistic networks exhibits characteristics of clustering and regularity. Consequently, routing algorithms based on communities have become a current research hotspot. However, existing community-based routing algorithms do not comprehensively analyze both the overlap of node communities and the impact of neighboring nodes on message transmission. To address this issue, this paper proposes a novel opportunistic routing algorithm called CWON, based on overlapping communities and communication willingness. First, we utilize the PercoMCV method to partition overlapping communities. Subsequently, we define the concept of communication willingness and design the CWON algorithm based on this concept. The CWON algorithm effectively addresses the problem of overlapping community partitioning and measures the importance of different neighboring nodes in message transmission. Our simulation results demonstrate that the CWON algorithm significantly enhances the success rate of message delivery while reducing routing overhead.

Opportunistic network nodes exhibit social attributes, and existing community routing algorithms are currently designed for situations where the community structure remains fixed and do not comprehensively analyze the impact of node location information on data forwarding. Over time, the community division results do not match the current network topology structure, and it becomes challenging to select appropriate relay nodes for forwarding. In order to solve this problem, this paper proposes a community routing based on node location information-CRLI. Firstly, the communities are partitioned based on node interaction information, and then the regional affiliation and regional connectivity are defined. Based on these, the CRLI algorithm is designed to comprehensively analyze the influence of node location, movement direction, and dynamic changes in community structure on data forwarding. The experimental results show that the CRLI algorithm can effectively improve the message delivery rate and reduce overhead.

Forecasting human trajectory is an essential technology in intelligent surveillance systems, robot navigation systems, autonomous driving systems, etc. Most of the trajectory prediction models based on RNN and Transformers use autoregressive methods to generate future trajectories, which may accumulate displacement errors and are inefficient for training and testing. To address these problems, we propose a novel decoder named MRG decoder, which introduces a Mapping-Refinement-Generation structure to generate trajectory in a non-autoregressive manner. Furthermore, we design the MRGTraj trajectory prediction model based on the proposed MRG decoder. Firstly, we employ a Transformer as an encoder to extract encoded features from the past trajectory. Secondly, we introduce an interaction-aware latent code generator to learn a Gaussian distribution from the social context among pedestrians for latent code sampling. Finally, we feed the encoded features to the MRG decoder and sample the latent code multiple times from the learned Gaussian distribution, providing additional inputs to the MRG decoder to generate multiple socially acceptable future trajectories. Experimental results on two public datasets, ETH and UCY, validate the effectiveness of the MRGTraj model. Besides, the MRGTraj model achieves superior prediction performance, with improvements of 13.21% on FDE metrics and a 71.29% speed-up compared to state-of-the-art models. The code is available athttps://github.com/wisionpeng/MRGTraj.

Edge computing deploys edges close to end-users to provide highly accessible resources and latency-sensitive services. It is invaluable for urban crowd/hazard management services, e.g., real-time dynamic route planning and hazard monitoring/analysis, etc. However, in such scenarios, various types of urban hazards jeopardize the usability of edge servers. Worsely, these hazards could be integrated, like gas fires caused by urban earthquakes. In this regard, the formulation of usability risks that servers face is intractable due to the complexity, incomplete real-time data and insufficient expert knowledge of these integrated hazards. Therefore, we innovatively define the usability risks asServer Hazard Riskmodel from the view of the spatial data field by utilizingInformation Diffusion techniquewhich can overcome the adverse conditions above. Then we involve it to formulate theServer Hazard Risk User Allocation(SR-UA) problem, and analyze three typical solutions from the perspective of optimality and efficiency, which are the Lexicographic Goal Programming approach (SR-UA-LGP), the Approximation approach (SR-UA-A) and the Particle Swarm Optimization-based approach (SR-UA-PSO). The extensive experiments based on two real-world datasets illustrate the superior performance of our model and solutions.

Opportunistic networks are mobile self-organizing networks that use the encounter opportunities brought by node movement to achieve communication. However, existing opportunistic routing algorithms rarely consider node context information and cache management at the same time, which leads to network congestion and high energy consumption problems in opportunistic networks. To solve the above problems, this paper defines the node historical activity degree and encounter duration based on the context information of nodes, and designs the AD-AC (historical Activity degree and encounter Duration of nodes-Acknowledgment deletion mechanism) opportunistic routing algorithm based on the context information of nodes by incorporating ACK (Acknowledgment) deletion mechanism. The simulation results indicate that AD-AC can substantially improve the message delivery rate while reducing the network overhead as well as the average hop count of messages.

As a kind of mobile traffic offloading technology, opportunistic triage downloads and distributes data through seed nodes. Therefore, how to efficiently and accurately select suitable seed nodes becomes a key problem of opportunistic streaming technology. To address the problem of insufficient research on seed node content coverage in existing studies, this paper combines the characteristics of opportunity networks and the solution idea of the influence maximization problem and proposes the evaluation model of node influence for the first time. Then proposes a seed node selection algorithm (SNSNI) based on node influence on this basis. Experimental results show that the set of seed nodes selected by SNSNI algorithm can obtain smaller average message transmission delay and more covered nodes in the triage scenario compared with the random algorithm, and fewer seed nodes are required to achieve the same effect.

Abstract Bubble diagrams serve as a crucial tool in the field of architectural planning and graphic design. With the surge of Artificial Intelligence Generated Content (AIGC), there has been a continuous emergence of research and development efforts focused on utilizing bubble diagrams for layout design and generation. However, there is a lack of research efforts focused on bubble diagram generation. In this paper, we propose a novel generative model, BubbleFormer, for generating diverse and plausible bubble diagrams. BubbleFormer consists of two improved Transformer networks: NodeFormer and EdgeFormer. These networks generate nodes and edges of the bubble diagram, respectively. To enhance the generation diversity, a VAE module is incorporated into BubbleFormer, allowing for the sampling and generation of numerous high‐quality bubble diagrams. BubbleFormer is trained end‐to‐end and evaluated through qualitative and quantitative experiments. The results demonstrate that BubbleFormer can generate convincing and diverse bubble diagrams, which in turn drive downstream tasks to produce high‐quality layout plans. The model also shows generalization capabilities in other layout generation tasks and outperforms state‐of‐the‐art techniques in terms of quality and diversity. In previous work, bubble diagrams as input are provided by users, and as a result, our bubble diagram generative model fills a significant gap in automated layout generation driven by bubble diagrams, thereby enabling an end‐to‐end layout design and generation. Code for this paper is at https://github.com/cgjiahui/BubbleFormer .

Abstract Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT‐based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area‐controlled polygonal facets. Based on this, we impose the variable‐capacity constraint and centroid constraint to the PowerRTF, providing the trade‐off between mesh quality and computational efficiency. Moreover, we apply a normal verification‐based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation instructions for those who want to evacuate and crowd flow updates for those who want to rescue, etc. In such scenarios, the behaviors of different roles (like rescuers and evacuees) and the positive/negative interactions among them must be considered in user allocation for reducing injuries and fatalities. In this paper, these issues are defined as a novelRole-Based Criticality(RBC) model to describe the fatal risks of different roles in the crowd/hazard scenarios. Based on the model, theRole-Based User Allocation(RUA) problem is formulated. To tackle this problem, we devise an optimal solution named RUA-ILP based on Integer Linear Programming. To accommodate large-scale scenarios, we propose two representative approximate approach named RUA-A and RUA-GA to ensure efficient and effectiveness user allocation respectively. They can maximize the overall role-based criticality which can reduce injuries and fatalities in crowd/hazard scenarios by theoretical proofing and extensive experiments conducted on a real-world dataset.

Edge computing provides a novel computing paradigm by deploying services on edge servers to serve nearby end-users with low latency. In this regard, a suitable allocation strategy is crucial that maximizes the number of users served at the minimum overall cost, which is referred to as the Edge User Allocation (EUA) problem. However, when edge computing meets public safety, some critical issues have not been fully considered by existing EUA approaches. Among these issues, the levels of danger to individuals quantitatively indicate whether individuals are in danger in an emergency. Hence, the inclusion of these levels impacts the priority for allocating resources in the EUA problem. In this paper, these levels are defined as individual criticalities formally. Then, we take them into account to formulate the novel CRiticality-EUA (CR-EUA) problem, and prove its NP-hardness. To solve this problem, an optimal approach, named CR-EUA-O, is proposed by utilizing the Integer Programming technique. Furthermore, we propose an approach with a proven approximation ratio, named CR-EUA-H, as an effective and efficient solution. Experiments are conducted on a real-world dataset to evaluate our approaches against four representative approaches. The results show the superior performance of our approaches in the overall criticality and execution time.

Floorplan generation has drawn widespread interest in the community. Recent learning-based methods for generating realistic floorplans have made significant progress while a complex heuristic post-processing is still necessary to obtain desired results. In this paper, we propose a novel wall-oriented method, called WallPlan , for automatically and efficiently generating plausible floorplans from various design constraints. We pioneer the representation of the floorplan as a wall graph with room labels and consider the floorplan generation as a graph generation. Given the boundary as input, we first initialize the boundary with windows predicted by WinNet. Then a graph generation network GraphNet and semantics prediction network LabelNet are coupled to generate the wall graph progressively by imitating graph traversal. WallPlan can be applied for practical architectural designs, especially the wall-based constraints. We conduct ablation experiments, qualitative evaluations, quantitative comparisons, and perceptual studies to evaluate our method's feasibility, efficacy, and versatility. Intensive experiments demonstrate our method requires no post-processing, producing higher quality floorplans than state-of-the-art techniques.

Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests’ occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.

Cloud computing promises an open environment where customers can deploy IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers may exist. Such service providers may record service information in a service process from a customer and then collectively deduce the customer's private information. Therefore, from the perspective of cloud computing security, there is a need to take special actions to protect privacy at client sides. Noise obfuscation is an effective approach in this regard by utilising noise data. For instance, it generates and injects noise service requests into real customer service requests so that service providers would not be able to distinguish which requests are real ones if their occurrence probabilities are about the same. However, existing typical noise generation strategies mainly focus on the entire service usage period to achieve about the same final occurrence probabilities of service requests. In fact, such probabilities can fluctuate in a time interval such as three months and may significantly differ than other time intervals. In this case, service providers may still be able to deduce the customers' privacy from a specific time interval although unlikely from the overall period. That is to say, the existing typical noise generation strategies could fail to protect customers' privacy for local time intervals. To address this problem, we develop a novel time-series pattern based noise generation strategy. Firstly, we analyse previous probability fluctuations and propose a group of time-series patterns for predicting future fluctuated probabilities. Then, based on these patterns, we present our strategy by forecasting future occurrence probabilities of real service requests and generating noise requests to reach about the same final probabilities in the next time interval. The simulation evaluation demonstrates that our strategy can cope with these fluctuations to significantly improve the effectiveness of customers' privacy protection.

SUMMARY Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science in the cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay‐for‐use model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such we develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one cloud service provider. The strategy has significant research merits, i.e. it achieves a cost‐effective trade‐off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data sets' usages. Meanwhile, the strategy also takes the users' tolerance of data accessing delay into consideration. We utilize Amazon's cost model and apply the strategy to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly. Copyright © 2010 John Wiley & Sons, Ltd.

SUMMARY Cloud promises users that they can present and deploy IT services in a pay‐as‐you‐go fashion in an open and virtualized cloud environment while saving huge capital investment in their own IT infrastructure. In this sense, protection of users' privacy is critical and has become one of the most concerned issues as otherwise users may eventually lose the confidence and passion of deploying cloud in practice. Under some special cloud circumstances, some users' privacy, such as plans or habits, could be induced from their service requests by service providers without permissions from users. In this regard, obfuscation strategy can protect this kind of privacy by injecting ‘noise’ service requests to confuse potential ‘immoral’ service providers. However, existing noise obfuscation strategies focus on single noise injection whereas investigation of noise injection architecture has been neglected. Especially, a common service pattern in inter‐clouds environment, the cooperative service process including different service providers, makes the risk of privacy serious and uncontrollable by the spread of users' privacy. To address this, we present a novel trust‐based noise injection strategy for privacy protection in cloud. To support the strategy, we describe our noise injection architecture in cloud which specializes in the relations between various service roles in inter‐clouds based on our trust model. The simulation can demonstrate that our noise injection strategy could significantly improve the effectiveness of privacy protection. Copyright © 2011 John Wiley & Sons, Ltd.

Due to the dynamic nature of cloud computing, how to achieve satisfactory QoS (Quality of Service) in cloud workflow systems becomes a challenge. Meanwhile, since QoS requirements have many dimensions, a unified system design for different QoS management components is required to reduce the system complexity and software development cost. Therefore, this paper proposes a generic QoS framework for cloud workflow systems. Covering the major stages of a workflow lifecycle, the framework consists of four components, viz. QoS requirement specification, QoS-aware service selection, QoS consistency monitoring and QoS violation handling. While there are many QoS dimensions, this paper illustrates a concrete performance framework as a case study and briefly touches others. We also demonstrate the system implementation and evaluate the effectiveness of the performance framework in our cloud workflow system.