Federated Self-Supervised Learning (FSSL) combines the advantages of decentralized modeling and unlabeled representation learning, serving as a cutting-edge paradigm with strong potential for scalability and privacy preservation. Although FSSL has garnered increasing attention, research indicates that it remains vulnerable to backdoor attacks. Existing methods generally rely on visually obvious triggers, which makes it difficult to meet the requirements for stealth and practicality in real-world deployment. In this paper, we propose an imperceptible and effective backdoor attack method against FSSL, called IPBA. Our empirical study reveals that existing imperceptible triggers face a series of challenges in FSSL, particularly limited transferability, feature entanglement with augmented samples, and out-of-distribution properties. These issues collectively undermine the effectiveness and stealthiness of traditional backdoor attacks in FSSL. To overcome these challenges, IPBA decouples the feature distributions of backdoor and augmented samples, and introduces Sliced-Wasserstein distance to mitigate the out-of-distribution properties of backdoor samples, thereby optimizing the trigger generation process. Our experimental results on several FSSL scenarios and datasets show that IPBA significantly outperforms existing backdoor attack methods in performance and exhibits strong robustness under various defense mechanisms.

In audio-visual joint analysis, generalized zero-shot learning (GZSL) aims to recognize unseen categories by aligning semantic information across modalities. However, significant challenges arise from temporal and semantic discrepancies between audio and video modalities. Traditional methods typically depend on static semantic embeddings, thereby overlooking the dynamic nature of these modalities and often resulting in modality imbalance. We propose the Dual-Factor Compensatory Clustering Network (DFCNet), an end-to-end framework for dynamic fusion and optimized alignment of heterogeneous modal information to address these limitations. DFCNet employs a multi-branch architecture, integrating a parallel multi-layer perceptron (MLP) for semantic modeling and a Bidirectional Long Short-Term Memory (BiLSTM) network for capturing temporal consistency. The Compensatory Fusion Block (CFB) employs tensor decomposition to facilitate cross-modal coupling, where the low-rank representation decomposition aligns intra-modal feature distributions. Additionally, we introduce the Dual-Factor Clustering Multi-objective Optimization Framework (DCMOF), which ensures gradient equilibrium and adaptively adjusts modality contribution weights to strengthen robust cross-modal alignment. Designed as a pluggable module, DFCNet can be seamlessly integrated into existing base models. Experimental results demonstrate that our framework significantly improves the performance of the Audio-Visual Cross-Modal Alignment (AVCA) model across multiple GZSL datasets. Ablation studies further validate the critical role of CFB in cross-modal alignment and highlight the significance of DCMOF in optimizing modality coordination. The code is available at https://github.com/ATKEROM/DFCNet.

Bandwidth resources are crucial for the internal communication and task execution of multi-UAV systems. However, traditional allocation models for multi-UAV system are inefficient and result in low utility of UAVs. To overcome this challenge, this paper proposes a novel bandwidth-oriented task allocation model that accounts for the supply-demand relationship between UAVs and tasks for bandwidth resources. To solve this model, this paper presents a Double Auction-based Task Allocation Mechanism, which can achieve distributed allocation of tasks and bandwidth among multi-UAV. The main steps of this mechanism are as follows: (1) each UAV privately submits its bandwidth demand and bids for each task; (2) each task determines the bandwidth price and initial allocation based on the bids, and announces them to each UAV; (3) each UAV selects the task that maximizes its utility, and obtains the final allocation. Furthermore, this paper proves that this mechanism satisfies desirable economic properties such as individual rationality, budget balance, and incentive compatibility. Finally, experimental results show that the proposed mechanism exhibits excellent performance in terms of UAV and task utility, as well as the rate of bandwidth allocation.

Code comments are crucial for program comprehension, and the automated generation of comments greatly enhances the efficiency of code commenting. Statement-level code summarization represents the finest granularity of code summarization, typically encompassing explanations of the purpose and functionality of code statements. Large Language Models (LLMs) are deep learning models trained on massive text data. They possess not only the capability to generate natural language text but also to deeply understand the meaning of text, applicable to various natural language processing tasks such as text summarization, question answering, and translation. Currently, LLMs have demonstrated the ability to generate summaries for code. In this paper, we systematically investigate the capability of LLMs to generate statement-level code summaries. we construct a dataset for statement-level code summarization and evaluate the ability of large language models on statement-level code summarization. For further research, we investigate the impact of different prompting techniques. We also study the effect of the temperature parameter on the quality of generated summaries. Additionally, we compare large language models with the state-of-the-art pretrained model CodeT5 and find out that large language models have great potential to replace pre-trained models and become the new state-of-the-art models on statement-level code summarization. To ensure the reliability of automatic evaluation, we also conduct human evaluation. Our findings emphasize the transformative potential of LLMs on statement-level code summarization and the challenges yet to be overcome.

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser-visual-inertial odometry, visual-laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual-inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual-inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser-visual-inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual-laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal-spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser-visual-inertial odometry factors, visual-inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86 m and 3.23 m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

As an emerging technology, Mobile Edge Computing (MEC) can effectively address the problem of limited device resource supply. One of the important challenges for MEC is to design effective allocation and pricing mechanisms to improve buyer utility. Market equilibrium is another important goal, but Walrasian equilibrium and Conditional equilibrium only possible exist in non-strictly submodular valuation markets. Consequently, the RAP4MEC mechanism is proposed to allocate MEC resources and achieve two-price equilibrium (2PE) in the sub-additive valuation market. RAP4MEC ensures that each buyer is allocated at least one edge server and sets high and low prices to satisfy the 2PE. Moreover, theoretical analysis shows that RAP4MEC satisfies the properties of Incentive Compatibility, Individual Rationality, and Weak Budget Balance. The experimental results indicate that RAP4MEC performs better in terms of buyer utility and buyer satisfaction, and the approximation ratio of optimal social welfare is improved by 10.2% compared to the latest algorithm.

As COVID-19 continues to spread, people are unable to move freely when their residence region is temporarily lockdown, supplies cannot normally enter into such zones, leading to the shortage of supplies in these areas. Thus to ensure the delivery of supplies while reducing contact, the unmanned aerial vehicle (UAV) deliveries have become a common way. In order to efficiently use UAV resources and reduce energy loss in data transmission while performing the tasks, clustering is often used for achieving the above objectives, where the selected cluster heads centrally plan tasks so that reduce the communication times. However, problems such as unreasonable clustering, high energy consumption of cluster heads, and high mortality of cluster heads, directly lead the low cooperation efficiency and short life cycle of UAVs. Considering the nodes often died earlier through the k-means algorithm and ant colony algorithm, and highly dependent on the base station, these factors affect the working cycle and coordination efficiency of the UAVs. Facing the issues above, the cluster head selection algorithm of UAV based on game (CHSA) is proposed, where the mixed game model is adopted to select cluster heads for each region after regional division, and selecting the representative node to perform the cluster head selection algorithm, which help to reduce the energy consumption of each round of communication between nodes. Moreover, the key properties of the CHSA algorithm are proved, and the comparison experiment are conducted to prove the CHSA algorithm can effectively reduce energy consumption and prolong the network life cycle.

Intelligent vehicle driving systems aim to control the driving behavior of a vehicle in real time without human intervention by perceiving and monitoring the surrounding environment. Describing images of traffic scenes automatically, which is one of the key problems of intelligent vehicle driving technology, has drawn attention since its inception. In recent years, a variety of automatic image description technologies have been proposed, among which the attention-based encoder-decoder framework achieved good results. In this paper we will discuss the fusing of a variety of information from multiple aspects of the images of traffic scenes. First, we will introduce visual attention, text attention and image topics attention which generates the weighted visual features, the attentive text information and the global image topics information respectively. We will then propose an adaptive two-stage merging network based on an encoder-decoder framework, which can fully integrate the three kinds of information in two stages, while automatically calculating the proportions of the information at each time step. Numerous experiments conducted on COCO2014 and Flickr30K datasets have demonstrated the effectiveness and advantages of the proposed method.

SUMMARY At present, disputes caused by medical expenses are widespread. How to use information means to provide accurate prediction of medical expenses for serious illnesses has become a research hotspot. Gastric cancer is a common cancer. The key of its medical expenses prediction lies in the mining of repeated structures and the statistics of repeated execution times. In the existing process mining methods, repeated nodes are regarded as the same nodes, and only counted once. This paper changes the original dynamic‐service‐flow‐net into a dynamic‐medical‐path‐net by taking the repetition times of nodes into account. Then αtj algorithm and TNC algorithm are proposed to build the dynamic‐medical‐path‐net system to predict the medical expenses. A medical evaluation model is proposed to evaluate each alternative schemes comprehensively in order to get the best medical scheme, and then the predictive medical expense would be obtained. The proposed method has about 25% improved to the conventional methods.

Abstract The flexibility and high reliability of cloud computing have generated a tremendous amount of economic benefits. With the growing demand for cloud resources, the task about how to making users' demand adaptive to the supply of cloud resources during peak and nonpeak hours becomes extremely challenging. However, most of the existing mechanisms mainly focus on the allocation of cloud resources, and ignore the balance between supply and demand of cloud resources. To improve the situation, this paper presents a combinatorial auction model for the cloud resource allocation and consumption shifting problem, and then puts forward a truthful auction mechanism with two different price curve functions, that is, staircase and continuous price curve. The proposed mechanism can effectively allocate and shift cloud resources to alleviate the pressure of cloud infrastructure during peak hours. Both the theoretical analysis and simulation results show that the proposed mechanism guarantees desired properties including individual rationality, budget balance, truthfulness and computational efficiency. In addition, extensive simulation results also show that continuous price curve outperforms stair case price curve in terms of system efficiency and balance efficiency.

Summary With the proliferation of knowledge source on the internet as well as the widely professional agents, the knowledge interchange is drawing much attention. Ontology is recognized as the crucial technology due to their nature of sharing, formalization, and conceptualization to integrate and share the knowledge. In this paper, by interpreting and negotiating the communication content, a unified understanding of knowledge is formed; then, we can realize the interoperability between ontologies. We have developed the ontology automatic negotiation by agent elect protocol (AEP) to elect optimal participants and encourage agents to obey the protocol, concept mapping protocol (CMP) to find the corresponding concept mappings with the highest relevancy, and in addition, agent negotiation protocol (ANP). In ANP, we define the simultaneous negotiation protocol and agents' strategies to combine distributed ontologies interchange with agent negotiation. Finally, the implementation and preliminary results are given to verify the validity of the proposed ontology negotiation.

Consensus mechanism plays an important role in blockchain. At present, mainstream consensus mechanisms include proof of work (PoW), proof of stake (PoS), and delegated proof of stake (DPoS). PoW, as is widely used in virtual currency, results in significant energy consumption; PoS and DPoS are proposed to reduce energy waste caused by PoW, but their disadvantage is that they tend to create Matthew Effect (ME): “the rich get richer.” In order to balance the discourse power of new nodes and elder ones, this paper proposes a flexible consensus mechanism called proof of engagement (PoE), based on the activity and contribution of network nodes. We analyze the incentive compatibility of PoE from the perspective of mechanism design. In our simulation experiments, we tested the profit changes under PoW, PoS, and PoE. The results illustrate it is easier for new nodes to accumulate their profits under PoE than under PoW or PoS, so as to reduce the negative impacts of ME.

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

Job allocation is a common application of human resource competition in the market. The third-party platform plays a vital role in coordinating the supply relationship between job suppliers and job seekers in order to help the talent market to allocate resources reasonably. For maximal social welfare, this paper proposes and implements a greedy double auction mechanism (GDA) based on the descending order of bid differentials. In the process of auction, virtual positions are regarded as heterogeneous commodities, and greedy algorithm is used to solve the optimal solution which maximizes social welfare and satisfies constraints. GDA has the economic attributes of individual rationality, budget balance as well as authenticity by the theoretical proof. At the same time, the feasibility and effectiveness of the mechanism are verified by simulation experiments. The experimental results show that the GDA mechanism has higher social welfare and average utility of job seekers, which can make job providers and excellent job seekers have a stable foothold in the increasingly fierce human resources competition market.

In existing works, the filters designed for delayed genetic regulatory networks contain time delay. If the time delay is unknown, the filters do not work in practical applications. In order to overcome the shortcoming in such existing works, this paper investigates the filter design problem of genetic regulatory networks with unknown constant time delay, and a novel adaptive filter is introduced, which can estimate online not only unknown network parameters but also the unknown time delay. By the Lyapunove approach, it is shown that the estimating errors asymptotically converge to the origin. Finally, simulation results are presented to illustrate the effectiveness of the proposed new design method.

This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.

Summary Wound area changes over multiple weeks are highly predictive of the wound healing process. A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model‐based method for intensity inhomogeneity correction and a spectral properties‐based color correction method to overcome these obstacles. State‐of‐the‐art level set methods can segment objects well. However, such methods are time‐consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re‐initialization. To increase the speed of the algorithm further, we also include an additive operator‐splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real‐world images demonstrate the advantages of the proposed method over state‐of‐the‐art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. Copyright © 2016 John Wiley & Sons, Ltd.

As technology evolves and electricity demand rises, more and more research focus on the efficient electricity allocation mechanisms so as to make consumer demand adaptive to the supply of electricity at all times. In this paper, we formulate the problem of electricity allocation as a novel combinatorial auction model, and then put forward a directly applicable mechanisms. It is proven that the proposed mechanism is equipped with some useful economic properties and computational traceability. Our works offer potential avenues for the stduy about efficient electricity allocation methods in smart grid.

In the model of partnership formation, every agent is in hot pursuit of maximizing his own utility. Doft Talman and Zaifu Yang have revealed the necessary condition of the existence of partnership equilibrium, however, the methods of how to compute the equilibrium are not given. The paper presents a method to compute the partnership utility equilibrium based on socially optimal assignment. Firstly, the partnership groups with social optimality are exhausted in the all possible cooperative pairs, and then under the condition of the existence of equilibrium, an algorithm is given to get the Equilibrium Utility Vector so as to approach the equilibrium status after multiple iterations. This solution to computing partnership payoff equilibrium can be obtained in O(n2) time. At last, to verify the method given by this paper, an example of municipal water supply planning is given to illustrate the correctness and effectiveness.

Summary We study the cloud resource auction problem where users can bid for resource bundle containing heterogeneous types of virtual machines, and providers allocate virtual machines to their users through group price model. Compared with fixed price model, which is not always the best approach for trading resources as its economically inefficient and inflexible nature, the group price model possess the better flexibility and monetary benefits for auction participants (e.g., cloud providers and users). The proposed auction mechanism strategy‐proof group buying‐based auction mechanism, which formulates the problem of virtual machine allocation in clouds as a combinatorial auction problem, and holds some important property such as individual rationality, ex‐post budget balance, and truthfulness, meanwhile guaranteeing efficiency in both the provider's revenue and system efficiency. Extensive simulation results show that the proposed mechanism yields the allocation efficiency and computational tractability compared with the mechanism with fixed price model. Copyright © 2015 John Wiley & Sons, Ltd.

Program comprehension is one of the most frequently performed activities during software maintenance and evolution. In order to facilitate program comprehension, a variety of graphical models have been proposed in software engineering community to construct relationships between program elements. These graphical models are mostly used for understanding the system based on structural syntax dependencies between program elements. However, these graphical models fail to extract the functional or semantic features of the system. Thus, developers still cannot effectively identify the functional part in source code fit for their needs. This paper tries to fill this gap, and proposes a novel representation, program feature network (PFN), to identify the semantic features of the program at class level. PFN is generated based on the relational topic model, a hierarchical probabilistic model of networks. Based on PFN, the semantic features and the links between pairs of two classes in the program can be clearly shown. In addition, PFN can predict the possible links between the newly change request in existing program feature network rather than reconstructing the representation from the start.

In this paper, we extend the idea of noise shaping for dual-tree complex wavelet transform (DCWT) image coding to a perceptual-based noise shaping. In classical noise shaping, the spatial error information is compensated by adding it back into the whole DCWT domain, which allows the retained to coefficients have better capability to approximate the original image. The proposed perceptual noise shaping introduces a perceptual weight to the spatial errors. The weight involves the structural similarity (SSIM) measurement and other adjustment parameters to shape the spatial errors. Experimental results show that the perceptual noise shaping has better results for visual quality and provides higher SSIM index than classical noise shaping. For example, the proposed perceptual noise shaping achieves an overall SSIM of 0.891 for the 8 bit 512×512 “barbara” compared to 0.878 in classical noise shaping when 5000 coefficients are retained.

The researches of Web service fasten on composition process, and are lack of the formalized description on dynamic attributes of service component itself. Aiming to these, a new dynamic and concurrent model of Web service component is presented. Firstly, this paper analyzes the process of state transition of service component under certain actions, and then depicts the dynamic transition process of service component with the state transition graph. At last, the physical representation method in memory and the algorithms to judge equivalence of state transition graph are given. Comparing with existing researches, the dynamic evolvement of service component under the actions is considered, and the algorithm to judge equivalence of service components provides an effective tool for model verification.

This paper proposes the SSOA (Semantic Service-oriented Architecture) based on formal and distributed ontologies, and the knowledge baseframed by ontology and the fuzzy reasoning system oriented to agent cognition are considered to prescribe and treat the uncertainty of agent cognizing. The SSOA includes two essential components. Knowledge Base Supported by Formal Ontology and Agent-oriented Fuzzy Reasoning System. First, Knowledge Base Framed by Formal Ontology builds a concept hierarchy organized by inclusion relation, and uses those concepts to describe specific objects to form assertions of an application domain. Agent-oriented Fuzzy Reasoning System then incorporates plausibility degree into assertions of certain agent, and the alphabet of symbols, well-formed formulas, axioms and inferences rules are constructed respectively. The SSOA architecture constructs a fundament for services interaction and composition automatically under open environment.

A difficulty of sharing messages transferred among Web services is absence of common vocabulary, and the semantic organization and description of Web services is an important requirement for enabling the automatic discovery, selection, composition, execution and monitoring of Web services. This paper proposes a model for semantic-based interaction between Web services. The method defined some ontology-based vocabularies to describe different Web services and some mapping rules among those vocabularies. Compared with existing methods, the method considers the Web services together with the semantic interpretation, satisfiability and reasoning of distributed ontologies. So the method can ensure the share and reusage of term be used for describing parameters of Web services