Merging mechanisms, as a mature business model in the field of online advertising, refers to the practice where platforms sort and display sponsored ads provided by advertisers alongside organic results to users according to specific rules. However, in real-world industrial scenarios, advertisers are gradually adopting autobidding instead of manual bidding—they only need to provide high-level constraints like target Return-on-Spend (tROS) to the agent, which then bids on their behalf to maximize multi-round value. Existing studies often overlook this actual business form, resulting in suboptimal outcomes. Meanwhile, the coexistence of the same item in both ad and organic result forms within merging mechanisms further increases the complexity of the context. In terms of interests, advertisers aim to maximize conversion value, while platforms seek to increase the revenue while ensuring user experience, thereby enhancing reputation. Nevertheless, existing works often fail to address this multi-stakeholder challenge in the modern auto-bidding era. To address these issues, we introduce IMPACTNet, an end-to-end framework based on automated mechanism design that learns a unified allocation and pricing mechanism. IMPACTNet directly incorporates advertisers' tROS constraints, models complex contextual information using a transformer-based architecture, and introduces a learnable, state-aware de-duplication strategy. By formulating the design as a constrained optimization problem, our framework learns a mechanism that ensures Auto-bidding Incentive Compatibility (AIC), ensuring truthfully reporting tROS a dominant strategy. Extensive experiments on synthetic and large-scale industrial datasets demonstrate that IMPACTNet significantly outperforms established baselines, achieving a better balance of platform objectives, user experience, and advertiser tROS satisfaction.

Abstract Spatial skyline queries represent a specialized category of skyline queries, applicable in various domains such as facility location, crisis management, and travel or event planning. The emergence of secure spatial skyline queries carries substantial practical implications. In this paper, we address the challenge posed by the point-geometry dependency problem inherent in existing spatial skyline query algorithms. Our approach involves a transformative strategy that simplifies the query into a more tractable range query problem. Building on this transformation approach, we design an efficient and secure spatial skyline query method for encrypted data, which requires alternating between ciphertext and plaintext queries. To ensure both security and optimal performance, we execute plaintext queries within a trusted execution environment. Experiments demonstrate the efficiency and effectiveness of our approach.

Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information, demonstrating effectiveness and adaptability in applications like personalized streaming recommendations. However, when extending CB algorithms to their neural version (commonly referred to as Clustering of Neural Bandits, or CNB), they suffer from loss of plasticity, where neural network parameters become rigid and less adaptable over time, limiting their ability to adapt to non-stationary environments (e.g., dynamic user preferences in recommendation). To address this challenge, we propose Selective Reinitialization (SeRe), a novel bandit learning framework that dynamically preserves the adaptability of CNB algorithms in evolving environments. SeRe leverages a contribution utility metric to identify and selectively reset underutilized units, mitigating loss of plasticity while maintaining stable knowledge retention. Furthermore, when combining SeRe with CNB algorithms, the adaptive change detection mechanism adjusts the reinitialization frequency according to the degree of non-stationarity, ensuring effective adaptation without unnecessary resets. Theoretically, we prove that SeRe enables sublinear cumulative regret in piecewise-stationary environments, outperforming traditional CNB approaches in long-term performances. Extensive experiments on six real-world recommendation datasets demonstrate that SeRe-enhanced CNB algorithms can effectively mitigate the loss of plasticity with lower regrets, improving adaptability and robustness in dynamic settings.

Recently, many e-commerce platforms have favored presenting a mixed list of ads and organic content to users. The widely-used approach separately ranks ads and organic items, then sequentially inserts ads into the list of organic items. However, this method yields sub-optimal results. Firstly, it only ensures that each generated ad and organic item list achieves local optimality, while the predetermined insertion order fails to guarantee global optimality. Secondly, this approach overlooks the mutual effect between organic items and ads, resulting in an incomplete utilization of contextual information. Besides, it cannot prevent strategic behavior by advertisers. Therefore, we propose a context-aware integrated framework to address these issues. This framework applies automated mechanism design to integrated ad auctions for the first time. Specifically, it models ads and organic items simultaneously along with their contextual information and employs a learning-based approach to prevent advertisers from engaging in strategic behavior. Afterward, the framework directly generates a mixed list, enhancing the overall performance. We also propose Transformer encoder-based Integrated Contextual Net work (TICNet) to generate the optimal integrated contextual ad auction. Finally, we validate the effectiveness of TICNet on synthetic and real-world datasets. Our experimental results demonstrate that TICNet significantly outperforms baseline models across multiple metrics.

Abstract Federated learning (FL) often uses local differential privacy (LDP) to prevent leaking data privacy through gradients. However, due to the high dimension of gradients, LDP will encounter the problem of privacy budget explosion in the application, resulting in low accuracy of the training model. To overcome this shortcoming, we propose a differential privacy FL protocol incorporating a control matrix and double shuffles. The control matrix, generated by the analyzer, is responsible for governing the selection and upload of clients’ gradients. Double shufflers shuffle the control matrix and clients’ gradients, respectively, so that the control matrix is invisible to the server and the gradient is anonymous to the server. In addition, the existing differential private FL often uses the same clipping scale for gradients clipping to facilitate determining the noise scale. However, this will bring too many clipping errors for the large gradients and too many noise errors for the small ones. To solve these problems, we propose an adaptive clipping scheme. Experiments on the real-world datasets show that our proposed methods achieve higher testing accuracy.

The rapid expansion of the Internet of Things (IoT) has brought forth new challenges and opportunities in securely managing and sharing vast amounts of data generated by connected devices. Blockchain technology, with its decentralization, tamper-resistance, and traceability, offers a promising framework for IoT data sharing but struggles to safeguard smart contracts and sensitive data. Integrating trusted execution environments (TEEs) with blockchain addresses these concerns, enabling secure execution and communication via remote attestation. However, existing remote attestation methods face challenges, including incompatibility across heterogeneous TEEs, inefficiency under frequent authentication, and vulnerability to DoS attacks. To tackle these, we propose a blockchain-based unified remote attestation scheme for IoT. Our three-tier blockchain architecture—comprising a certificate authority (CA) channel, an authoritative channel, and a business channel—separates authentication, attestation, and operations while ensuring auditability. An abstraction layer supports heterogeneous TEEs, and an authoritative blockchain stores authentication reports, enabling secure, frequent attestations. Additionally, a distributed CA system enhances resilience to DoS attacks. Experimental results validate our scheme’s efficiency and security, offering a robust solution for IoT data sharing.

Serverless computing typically relies on the third-party forwarding method to transmit data between functions. This method couples control flow and data flow together, resulting in significantly slow data transmission speeds. This challenge makes it difficult for the serverless computing paradigm to meet the low-latency requirements of web services.

Emerging technologies, such as cloud computing and artificial intelligence, significantly arouse concern about data security and privacy. Homomorphic encryption (HE) is a promising invention, which enables computation on encrypted data without decrypting it so as to ensure data security and privacy. Nevertheless, computation within homomorphic encryption involves time-consuming operations, e.g., Number Theoretic Transform (NTT). The tremendous computation overhead is the critical obstacle in deploying HE applications widely. Besides, in order to meet the performance and security requirements of different applications, it is pivotal to design parametric NTT architecture. In this paper, we propose a flexible and parametric NTT accelerating scheme based on hybrid-radix butterfly, named FHNTT. Specifically, we construct high radix butterfly units and divide the computation of them into several stages such that every stage can be performed pipelined. The number of required twiddle factors declines with the increase of radix value. In addition, we adopt address offset strategy to reduce memory consumption. We implement FHNTT on FPGA due to its fine-grained parallel computing capabilities and customized architecture. Empirical results show that FHNTT has an improved performance compared with other NTT architectures and supports a wide range of parameters. Concretely, FHNTT achieves up to 1.99 × to 2.78 × improvement in latency over other FPGA implementations and the memory utilization rate is up to 94%. Moreover, the flexibility makes FHNTT applicable to multiple use cases.

Approaches predicting the results of mutation testing by machine learning have been proposed to reduce the cost of mutation testing. The predictive approaches based on PIE theory and approaches based on natural language have been proposed. However, both PIE-based and natural language-based approaches have disadvantages, leading to a reduction in effectiveness at the test case level prediction. In order to predict at the test case level and improve the effectiveness of prediction, we propose Natural Language and PIE Predictive Mutation Testing (NLPIE-PMT), which combines advantages of PIE-based and natural language-based approaches and predict whether each test case kills each mutant in the cross-version scenario. The experimental results on subjects in Defects4J show that NLPIE-PMT can predict whether each test case kill each mutant with the average F1-score of 0.811, which is 0.135 and 0.046 higher than the PIE-based baseline and the natural language-based baseline respectively. NLPIE-PMT also performs better than the baselines in predicting mutation score.

An online service system may experience various performance faults during operation. Detecting and locating these faults after they occur can significantly impact the user experience and lead to significant losses. Therefore, it is necessary to predict faults before they occur. Existing methods for fault prediction typically only predict the possibility of fault, without providing more granular predictions, such as the type of fault. This can make troubleshooting more difficult for developers. In this paper, we propose a fine-grained fault prediction method called FSFP, which not only predicts the possibility of fault but also identifies the type of fault that may occur. The method initially collects performance monitoring metrics from the runtime system, including two types: normal operation and abnormal conditions. It then utilizes cross-attention to capture the interdependencies between these two types of monitoring metrics, followed by the construction of a multi-label classification model. We evaluated FSFP by injecting faults into a benchmark microservice system. In terms of predicting the possibility of fault, FSFP achieved a precision of 0.999, a recall of 0.998, and an F1 score of 0.999. In terms of predicting the type of fault, FSFP achieved an exact match ratio of 0.955 and a Hamming loss of 0.017. In terms of predicting six specific types of faults, FSFP achieved four optimal F1 scores.

Federated learning is a new type of artificial intelligence technology. During the training process, the client transmits model parameter information instead of local data to ensure their privacy and security. But it also incurs higher communication costs. This article proposes a new federated learning pruning method, FedADP, with the aim of adaptively determining pruning ratios for each layer in each client model without infringing on client privacy, and achieving more accurate pruning effects. Our method not only reduces communication costs during the training process, but also maintains accuracy similar to the original model. We conducted experimental validation using classic models and datasets, and evaluated our scheme and traditional federated learning scheme in terms of model accuracy, communication cost, and computational cost.

Abstract We specifically present a general method for monocular fluid videos to reconstruct and edit 3D fluid volume. Although researchers have developed many monocular video‐based methods, the reconstructed results are merely one layer of geometry surface, lack of accurate physical attributes of fluids, and challenging to edit fluid. We obtain a high‐quality 3D fluid volume by extending the smoothed particle hydrodynamics (SPH) model with external force guidance. For reconstructing fluid, we design target particles that are recovered from the shape from shading (SFS) method and initialize fluid particles that are spatially consistent with target particles. For editing fluid, we translate the deformation of target particles into the 3D fluid volume by merging user‐specified features of interest. Separating the low‐ and high‐frequency height field allows us to efficiently solve the motion equations for a liquid while retaining enough details to obtain realistic‐looking behaviours. Our experimental results compare favourably to the state‐of‐the‐art in terms of global fluid volume motion features and fluid surface details and demonstrate our model can achieve desirable and pleasing effects.

Abstract Microtask crowdsourcing is a form of crowdsourcing in which work is decomposed into a set of small, self-contained tasks, which each can typically be completed in a matter of minutes. Due to the various capabilities and knowledge background of the voluntary participants on the Internet, the answers collected from the crowd are ambiguous and the final answer aggregation is challenging. In this process, the choice of quality control strategies is important for ensuring the quality of the crowdsourcing results. Previous work on answer estimation mainly used expectation–maximization (EM) approach. Unfortunately, EM provides local optimal solutions and the estimated results will be affected by the initial value. In this paper, we extend the local optimal result of EM and propose an approximate global optimal algorithm for answer aggregation of crowdsourcing microtasks with binary answers. Our algorithm is expected to improve the accuracy of real answer estimation through further likelihood maximization. First, three worker quality evaluation models are presented based on static and dynamic methods, respectively, and the local optimal results are obtained based on the maximum likelihood estimation method. Then, a dominance ordering model (DOM) is proposed according to the known worker responses and worker categories for the specified crowdsourcing task to reduce the space of potential task-response sequence while retaining the dominant sequence. Subsequently, a Cut-point neighbor detection algorithm is designed to iteratively search for the approximate global optimal estimation in a reduced space, which works on the proposed dominance ordering model (DOM). We conduct extensive experiments on both simulated and real-world datasets, and the experimental results illustrate that the proposed approach can obtain better estimation results and has higher performance than regular EM-based algorithms.

In the paper, the effect of the actuator lag and communication delays on the stability of a vehicle platoon, which adopts a constant spacing policy and predecessor-leader following type information flow is discussed. While using spacing error transfer function, we analyzed the string stability in frequency domain, and the upper bounds on the allowable parasitic lag and communication delays could be confirmed. Also, With the larger post-sequence delay, the limited string scalability would be shown by the communication delay of the leader.

As advanced computing and communication technologies penetrate every aspect of our life, we have witnessed the persistent growth of open systems where entities interact with one another without prior knowledge or experiences. Trust becomes an important metric in such open systems. This paper presents a dependable trust management scheme-GroupTrust, and a working system to support GroupTrust. It makes three original contributions. First, we identify a set of vulnerabilities that are common in existing reputation based trust models. We show that reputation trust built solely on direct experiences or by combining direct experiences with uniform trust propagation can be vulnerable. Second, we develop GroupTrust, a dependable trust management scheme to provide reliable trust management in the presence of dishonest ratings, malicious camouflage, and malicious collusive behaviors. The GroupTrust scheme is novel in two aspects: (i) we develop a pairwise similarity based feedback credibility to enhance the resilience of trust computation in the presence of dishonest ratings; (ii) we propose to propagate trust based on a Susceptible-Infected-Recovered (SIR) model, which defines trust propagation threshold to control how trust should be propagated. Finally, we evaluate the effectiveness of GroupTrust against fourthreat models using both simulated and real world datasets. Our experimental results show that feedback credibility based local trust computation can effectively constrain strategically malicious participants from taking advantages of their dishonest ratings. SIR-based trust propagation control enables safe trust propagation and blocks irrational trust propagation. We show that GroupTrust scheme significantly outperforms other trust models in terms of both performance and attack resilience in the presence of dishonest feedbacks, sparse feedbacks, and strategically malicious participants against four representative threat models.

Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this article, we present ServiceTrust ++ , a feedback quality--sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Compared with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies, and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants’ feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on the feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers from boosting their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine a trust-decaying strategy with a threshold value--based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedback. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

Service network analysis is an essential aspect of web service discovery, search, mining and recommendation. Many popular web service networks are content-rich in terms of heterogeneous types of entities, attributes and links. A main challenge for ranking services is how to incorporate multiple complex and heterogeneous factors, such as service attributes, relationships between services, relationships between services and service providers or service consumers, into the design of service ranking functions. In this paper, we model services, attributes, and the associated entities, such as providers, consumers, by a heterogeneous service network. We propose a unified neighborhood random walk distance measure, which integrates various types of links and vertex attributes by a local optimal weight assignment. Based on this unified distance measure, a reinforcement algorithm, ServiceRank, is provided to tightly integrate ranking and clustering by mutually and simultaneously enhancing each other such that the performance of both can be improved. An additional clustering matching strategy is proposed to efficiently align clusters from different types of objects. Our extensive evaluation on both synthetic and real service networks demonstrates the effectiveness of ServiceRank in terms of the quality of both clustering and ranking among multiple types of entity, link and attribute similarities in a service network.

This paper argues that trust and reputation models should take into account not only direct experiences (local trust)and experiences from the circle of ”friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrus