As privacy protection gains increasing importance, more models are being trained on edge devices and subsequently merged into the central server through Federated Learning (FL). However, current research overlooks the impact of network topology, physical distance, and data heterogeneity on edge devices, leading to issues such as increased latency and degraded model performance. To address these issues, we propose a new federated learning scheme on edge devices that called Federated Learning with Encrypted Data Sharing(FedEDS). FedEDS uses the client model and the model's stochastic layer to train the data encryptor. The data encryptor generates encrypted data and shares it with other clients. The client uses the corresponding client's stochastic layer and encrypted data to train and adjust the local model. FedEDS uses the client's local private data and encrypted shared data from other clients to train the model. This approach accelerates the convergence speed of federated learning training and mitigates the negative impact of data heterogeneity, making it suitable for application services deployed on edge devices requiring rapid convergence. Experiments results show the efficacy of FedEDS in promoting model performance.

Traditional DRAM-based memory solutions face challenges, including high energy consumption and limited scalability. Non-Volatile Memory (NVM) offers low energy consumption and high scalability. However, security challenges, particularly data remanence vulnerabilities, persist. Prevalent methods such as the Bonsai Merkle Tree (BMT) are employed to ensure data security. However, the consistency requirements for integrity tree updates have led to performance issues. It is observed that compared to a secure NVM system without persistent secure metadata, the average overhead for updating and persisting the BMT root with persistent secure metadata is as high as 2.48 times. Therefore, this paper aims to mitigate these inefficiencies by leveraging the principle of memory access locality. We propose the Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree (DCAS-BMT). The DCAS-BMT is dynamically built and continuously adjusted at runtime according to access weights, ensuring frequently accessed memory blocks reside on shorter paths to the root node. This reduces the verification steps for frequently accessed memory blocks, thereby lowering the overall cost of memory authentication and updates. Experimental results using the USIMM memory simulator demonstrate that compared to the widely used BMT approach, the DCAS-BMT scheme shows a performance improvement of 34.1%.

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favorite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort1to find a trade-off solution among user-side fairness, provider-side fairness, and personalized recommendations utility. Previous works habitually treat this issue as a knapsack problem, incorporating both-side fairness as constraints. In this paper, we adopt a novel perspective, treating each recommendation list as a runway rather than a knapsack. In this perspective, each item on the runway gains a velocity and runs within a specific time, achieving re-ranking for both-side fairness. Meanwhile, we ensure the Minimum Utility Guarantee for personalized recommendations by designing a Binary Search approach. This can provide more reliable recommendations compared to the conventional greedy strategy based on the knapsack problem. We further broaden the applicability of FairSort, designing two versions for online and offline recommendation scenarios. Theoretical analysis and extensive experiments on real-world datasets indicate that FairSort can ensure more reliable personalized recommendations while considering fairness for both the provider and user.

The emergence of the mobile edge computing paradigm enables the deployment of microservices on edge servers, which greatly improves the quality of services and reduces network transmission costs. However, due to limited computing and storage resources, an individual edge server can host only a limited number of microservice instances. Moreover, user mobility often results in uneven distribution of service requests in mobile edge computing systems. To this end, it is a key problem to dispatch microservice requests to appropriate edge servers to minimize the average service response time. Current solutions to this problem rely on centralized methods and suffer from serious problems of single point of failure, error-proneness, difficult expansion, low robustness, etc. To resolve these problems, this paper proposes a decentralized game-theoretic approach for dispatching microservice requests effectively and efficiently in mobile edge computing systems. Specifically, we formulate the request dispatching problem as a decentralized non-cooperative game and propose a decentralized request dispatching algorithm that can find the Nash equilibrium through finite iterations. We conduct a series of experiments to demonstrate that our approach beats benchmarking approaches with close-to-optimal performance and high efficiency measured by convergence time.

Financial institutions may be subject to financial fraud by malicious users because of the large amount of transaction data and sensitive user information involved. Therefore, it is crucial to design a machine learning model that can detect abnormal data in financial institutions. However, with the development of the economy and technology, the massive amount of user-generated data is distributed among various financial institutions, and how to enable multiple financial institutions to collaborate on anomalous data detection has become a new challenge. In this paper, we propose a blockchain-based federated learning architecture to assist multiple financial institutions to collaborate on anomaly detection. First, anomaly detection models are trained locally without sharing local data, which effectively protects data privacy. Second, the architecture introduces a differential privacy algorithm to protect data security in communication. Finally, to avoid communication bottlenecks that threaten data security, the architecture employs the aperiodic aggregation algorithm in which clients collaborate to reduce communication costs. Experimentally, a large number of experiments are conducted using three datasets to evaluate the proposed architecture. The experimental results show that the architecture is effective in detecting anomalous data and reducing communication costs.

With the rapid development of mobile internet, mobile data traffic has increased substantially, and multimedia services have gradually occupied a large proportion of mobile network traffic. Using the Device-to-Device (D2D) technique for multimedia content sharing is considered as an effective solution to address large-scale user requests. D2D communication technology refers to direct data transmission between two adjacent users by reusing the spectrum resources of cellular users, effectively improving system throughput and spectrum resource utilization. Currently, most literature on D2D content sharing focuses on cache strategy research, while neglecting the design of reasonable content sharing mechanism. This paper focuses on the D2D service supply problem in content sharing scenarios and designs a stable sharing strategy by jointly considering power control and spectrum allocation. The power control and spectrum allocation problems for D2D users and cellular users are modeled as a minimum cost flow problem in graph theory and solved using the minimum cost flow algorithm. To ensure the stability of the content sharing system, a stable matching algorithm based on the delay acceptance algorithm is proposed for requesters and providers, considering preference factors. A series of experiments show that the proposed algorithm has good fairness and execution efficiency.

With the rapid development of mobile communication and Internet of Things (IoT) technologies, smart mobile devices such as portable and sensor devices have been widely used in our daily lives. However, their compact size and limited energy capacity inherently hinder their ability to efficiently handle computation-intensive tasks within acceptable timeframes. To tackle this challenge, computation offloading has emerged as a pivotal solution. Computation offloading can significantly reduce the response time and energy consumption for mobile devices executing such tasks. However, it also bring some challenges, notably the risk of compromising user privacy. In this paper, we prioritize user privacy alongside considerations of service delay and energy consumption, and model the offloading decision problem as a multi-objective optimization problem. We employ an enhanced multi-objective bat algorithm to identify Pareto front solutions, balancing the diverse objectives effectively. Our experimental validation confirms the feasibility and efficacy of the proposed method, offering a promising avenue for addressing the complexities of computation offloading in mobile edge computing systems.

Data service marketplaces are pivotal elements of an intelligent society as they bridge user requirements and the realization of value from data services. Ensuring the efficient circulation of data services and driving industrial chains to create value within data service marketplaces has become a core issue in developing intelligent societies, posing an urgent concern for researchers and governments. Within the context of service ecosystems, individual data services can no longer meet the requirements of users and organizations thus moving towards convergence, which brings new governance challenges both economically and technically. This paper comprehensively explores the governance of data service marketplaces by combining insights from service ecosystems and data service value. It begins by introducing data service marketplaces. Data servitization and standardized workflows are driving the massive expansion of data services marketplaces. Then it analyzes the complexity of marketplaces under service ecosystems. Furthermore, the governance issues and research themes of data service marketplaces were elaborated at macro, medium, and micro scales. This paper provides new ideas for the value creation and development of data service marketplaces.

Abstract Mobile application service ecosystems (MASEs) are highly complex systems that involve multiple factors influencing value creation. However, current research on the impact of structural holes (SHs) as an organizational characteristic on value creation in MASE is still insufficient. To address this research gap, this paper proposes a value creation model for MASE and investigates the impact of SH on the value creation of MASE. Moreover, this paper investigates the correlation between SH and diversity, which can facilitate regulating the value creation process in MASE. Finally, we construct a computational experiment, comparing and analyzing how changes in SH affect the value creation of MASE and the correlation between SH and diversity. The findings of this study can be used to induce the evolution of MASE and promote its value maximization.

Multi-scale detection based on Feature Pyramid Networks (FPN) has been a popular approach in object detection to improve accuracy. However, using multi-layer features in the decoder of FPN methods entails performing many convolution operations on high-resolution feature maps, which consumes significant computational resources. In this paper, we propose a novel perspective for FPN in which we directly use fused single-layer features for regression and classification. Our proposed model, You Only Look One Hourglass (YOLOH), fuses multiple feature maps into one feature map in the encoder. We then use dense connections and dilated residual blocks to expand the receptive field of the fused feature map. This output not only contains information from all the feature maps, but also has a multi-scale receptive field for detection. The experimental results on the COCO dataset demonstrate that YOLOH achieves higher accuracy and better run-time performance than established detector baselines, for instance, it achieves an average precision (AP) of 50.2 on a standard 3× training schedule and achieves 40.3 AP at a speed of 32 FPS on the ResNet-50 model. We anticipate that YOLOH can serve as a reference for researchers to design real-time detection in future studies. Our code is available at https://github.com/wsb853529465/YOLOH-main.

Service recommendations are crucial for developers to create mashups such as mobile applications, workflows, e-business solutions, etc. Existing methods based on collaborative filtering or content analysis are manual and cannot automatically acquire services that align with the requirements of mashup creation. A possible solution to automatically acquiring necessary services for mashups is the seq2seq (sequence to sequence) generation model, which has demonstrated promising performance in automatic text and program code generation. However, two main challenges must be tackled in service acquisition based on the seq2seq model. First, the seq2seq model can only acquire a set of services without inter-service dependencies, but such dependencies are crucial in the generation of sequences for services. Second, external knowledge must be leveraged to recommend services more accurately that fulfill developers' requirements, such as similar historical user requirements and combining mashup category information, due to the incomplete description of user requirements. To tackle these challenges, this paper proposes GSR (Generation ofServiceRecommendations), an approach that can automatically acquire services based on user requirements. Specifically, GSR employs reinforcement learning to learn the inter-dependencies among services and integrate dependencies into service recommendations. To further improve the quality of the acquired services, GSR retrieves relevant user requirements based on BERT (Bidirectional Encoder Representation from Transformers) to help identify potential services. Experiment results conducted on real-world datasets show the superior performance of GSR. Compared with the existing recommendation approaches, the precision metric is increased by up to 1.99x, and the recall metric is increased by up to 12%.

Federated Learning (FL) is a promising paradigm for mining massive data while respecting users' privacy. However, the deployment of FL on resource-constrained edge devices remains elusive due to its high resource demand. In this paper, unlike existing works that use expensive dense models, we propose to utilize dynamic sparse training in FL and design a novel sparse-to-sparse FL framework, named as POP-FL. The framework can reduce both computation and communication overheads while maintaining the performance of the global model. Specifically, POP-FL partitions massive clients into groups and performs parallel parameter exploration, i.e.,Parallel Over-Parameterization, over the collaboration between these groups. This exploration can greatly improve the expressibility and generalizability of sparse training in FL (especially for extreme sparsity levels) through reliably covering sufficient parameters and dynamically updating the global sparse network's structure during the training process. Experimental results show that compared with existing sparse-to-sparse training methods in both iid and non-iid data distribution, POP-FL achieves the best inference accuracy on various representative networks.

Social recommendations play a crucial role in providing personalized services to users by leveraging social relationships and user sessions. Despite recent advancements, it still faces challenges in dealing with social inconsistency and the loss of critical semantic information in user-service interactions. To overcome these problems, an Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users’ fresh interests. Specifically, the graph structure features on user-service interactions and the correlations between users and different sequences are extracted by the graph contrastive learning module. Then, social consistency sampling based on the graph convolutional network is adopted to filter out noise information effectively. Finally, time-sliced representations on the dual side (user, service) are integrated to capture users’ evolving interests by employing gated recurrent units. Comprehensive experiments on three datasets demonstrate the proposed model consistently outperforms the representative baseline methods in various evaluation metrics. EGCLSR facilitates the recommendation of services that fulfill instant requirements within dynamically evolving user interests.

Mobile Edge Computing (MEC) plays a crucial role in providing diverse computation and storage services to intelligent equipment. In recent years, the utilization of Unmanned Aerial Vehicles (UAVs) equipped with edge servers has emerged as a promising approach to enable ubiquitous edge computing services. This paradigm offers various benefits, such as reduced latency and flexible service provisioning. In the context of UAV-assisted edge computing, optimizing the location and trajectory of UAVs is vital, since the communication distance has significant impact on the communication rates between edge servers and devices. To address this optimization problem, this paper establishes a UAV-assisted edge computing system and focuses on investigating the path planning issue to expedite the offloading of computational tasks. In order to achieve this objective, we propose an improved tabu search algorithm that can efficiently optimize the number of UAVs, path planning. And to ensure reliable communication, we introduce reliability guarantees. Furthermore, we consider the energy limitations of UAVs to ensure practical feasibility. Through extensive simulations, we demonstrate that the proposed algorithm outperforms alternative approaches in terms of the specified objectives.

With the advent of mobile internet, Online-to-Offline (O2O) home services have emerged, such as home healthcare and repair services, greatly facilitating our lives. Customers book services through online platforms, and workers provide the requested services at the customers’ homes offline. However, each time workers travel to customers’ homes, they incur opportunity cost, leading to increased cost for home services. In this paper, we propose to bundle several O2O home service requests close to each other and match them with a worker. Therefore, requests that are in a bundle can split the worker’s opportunity cost. Specifically, we formalize the request bundling problem for O2O home services, which aims to minimize the overall cost of completing all requests while satisfying the time and Quality of Service(QoS) constraints. We present three Bi-layer Greedy request bundling(BiG) algorithms to solve it, including BiG-LEV, BiG-DIST, and BiG-COST. Besides, a cost accounting method based on Shapley value is designed to calculate the actual cost of each service for in-depth analysis. Finally, we illustrate a case of bundling requests for home healthcare services and compare the performance of the three algorithms.

Recommendation systems play a crucial part in helping users efficiently obtain information based on users’ current preferences and discover their individual needs, but the existing works are deficient in terms of the evolution of users’ interests. In this paper, Graph Embedding with Service and User information (GESU) model is proposed to address the limitations of capturing users’ fresh interests. Graph-structured data derived from time-varying session sequences are captured via gated graph neural networks. Then, the evolving influence of different services for users is obtained through a multi-head module. At the same time, a graph attention network is applied to predict users’ fresh consumption preferences by selecting representative friends to characterize user information. Extensive experiments on three datasets show that the proposed model outperforms state-of-the-art methods consistently on various evaluation metrics. GESU provides a means to recommend services that meet current requirements in an environment where users’ interests evolve dynamically.

Automatic function naming aims to generate a concise and meaningful name for a function, and has become a popular research area.Function naming models based on deeplearning have made significant progress in recent years.Most of the existing neural models represent a function based on the granularity of token or AST (Abstract Syntax Tree) node.However, generating function names requires more fine-grained knowledge of code, but the representation of tokens or AST nodes is not enough to capture global function semantics.In our work, we propose Apker, a novel Adaptive prior-knowledge-assisted function naming based on multi-level information explorer.The Apker includes three modules: Multi-level Information Explorer (MIE), Adaptive Prior Knowledge Adaptor (APKA) and the Generator.The MIE captures the function semantics from a local and global perspective, motivated by the understanding patterns of humans, who will first understand the meaning of each statement and then comb their logical relations to understand the whole function.The APKA uses the pre-retrieved prior knowledge to assist the model, motivated by our observation that certain name tokens can be extracted directly from certain statements and such probability differs significantly in different types of statements.Finally, the Generator generates function names.The experimental results demonstrate that our approach outperforms the baselines by 5.4% in Precision, 12.7% in Recall, and 7.4% in F1-score.

With the rise of crossover services, service providers usually cooperate with each other on different platforms to expand their service value. However, in cross-platform cooperation, insufficient understanding and malicious competition between different platforms would lead to inaccurate trust establishment and unreliable trust recommendations. In this paper, we propose a trust management framework of cross-platform based on blockchain to establish, store and recommend trust securely for cross-platform cooperation. Firstly, we take into account the contextual background information to enhance interaction and understanding between platforms to achieve accurate trust establishment. Secondly, the trust recommendation algorithm is written into the blockchain in the form of smart contracts, which can ensure the security of trust recommendation. Finally, experiments are used to demonstrate the superiority and reliability of the framework.

Trust prediction is gaining significant interest since it could reduce the burden of user decision-makings effectively in various social activities. Existing works on trust prediction mainly based on trust networks, however, usually give little consideration to data sparsity and temporal continuity of user behavior. In order to solve these problems, we propose a comprehensive deep MemTrust model for trust prediction. With this model, we introduce a embedding layer to extend the feature space and alleviate the distinctive information oblivion caused by data sparsity. In addition, Long Short-Term Memory(LSTM) network is utilized to extract overall time series features through the multiple time slices of user features. Finally, the trust is estimated by pairwise time series features of users. Extensive experiments are validated on two real datasets, which demonstrate that the proposed model has superior performance compared with representative baseline approaches.

The prosperous development of Internet services such as O2O, IoT, and Web API has brought new vitality to service commercial platforms. However, these services involve online and offline business, which are widely diverse without a unified design and development standard. In addition, Internet services update frequently, which leads to repeat releases on commercial platforms. Therefore, in this paper, we present a generic method to rapidly release Internet services on commercial platforms. The method uses a highly abstract metamodel to express service business extensively and realizes service functions by executing metamodel objects. This method has wide versatility. Meanwhile, it extends the DevOps theory to solve the frequent changes of service functions during use after the release. Finally, we verified the usability of this method in the elderly healthcare domain.

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

Mobile edge computing has significantly reduced the response time of mobile applications by executing services in close proximity to mobile consumers. However, the capabilities and resources of edge servers are typically limited; additionally, service requirements in mobile environments are becoming increasingly complex and diverse. In this context, properly dispatching service requests to edge and cloud servers to improve the quality of services has become a crucial problem. In this paper, we focus on this problem and aim to minimize the response time of service invocations in mobile edge computing systems. The problem is formulated as an optimization problem, and a heuristic algorithm that combines the Genetic algorithm and the simulated Annealing algorithm for service selection in Mobile Edge Computing systems (GAMEC) is proposed to solve the problem. A series of experiments has demonstrated that the method performs well in reducing the response time of service invocations in mobile edge computing systems. Moreover, the execution time of GAMEC is of a low order of magnitude, and the algorithm scales well as the experimental scale increases.

The emerging of mobile edge computing has significantly reduced the response time and Internet risk of service invocations. However, due to the distributed architecture and limited resources, balancing the load between edge servers to minimize the overall response time has become a critical objective for mobile edge computing. This problem is generally related to two aspects, request dispatching and service scheduling. To address this issue, we proposed a novel heuristic method called GASD (combined Genetic algorithm and simulated Annealing algorithm for Service request Dispatching). It tackles the problem by jointly conducting request dispatching and service scheduling. In addition, a solution combination algorithm is applied to reduce the computation complexity of the method. The experimental results show that the GASD method can achieve much lower overall response time than the compared methods. Moreover, the execution time of GASD is in a low order of magnitude and the algorithm performs excellent scalability as the experimental scale increases.

Due to the limited capabilities and resources, edge servers cannot meet the increasingly complex and diverse service requirements in mobile edge computing environments. In this circumstance, how to dispatch the component tasks of service requests to edge and cloud servers to reduce the time delay has become a crucial problem. Therefore, we focus on this problem and propose a heuristic algorithm called GAMEC (combined Genetic algorithm and simulated Annealing algorithm for service selection in Mobile Edge Computing systems). The simulated experiments have demonstrated the high effectiveness of the method.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

The development of mobile computing and cloud computing enables people to invoke various services from their mobile devices. This paper focuses on the problem of service composition with temporal and QoS constraints in mobile cloud computing. This problem indeed becomes dramatically tough due to the mobility environment, local-time constraints and global QoS constraints as well. This study aims to form such a service composition that not only satisfies both the time constraints and QoS constraints in a mobile service composition, but also ensures the composition to be executed successfully to the greatest extent in the uncertain mobile environment. Firstly, it formally defines the problem and then transfers the problem into a constrained optimization problem and proves it to be an NP-hard problem. To solve this problem, it proposes a differential evolutionary for constraint driven service composition algorithm (DE4CDSC). It first utilizes a constraints based service filtering process to reduce the searching space and then adopts a differential evolutionary based algorithm to form a service combination by maximizing its successful probability after considering service providers' mobility. To evaluate the proposed approach, a series of simulation experiments and comparisons are conducted to demonstrate the effectiveness of the proposed approach.

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

QoS as an important criterion has attracted more and more attention in the service selection process. Various QoS-aware service selection methods have been proposed in recent years. However, few of them take into account of the QoS correlations between services, causing several performance issues. QoS correlations can be defined as that some QoS attributes of a service are not only dependent on the service itself but are also correlated to other services. Since such correlations will affect QoS values, it is important to study how to select appropriate candidate services while taking into account of QoS correlations when generating composite services with optimal QoS values. To this end, we propose a novel method of service selection, called the correlation-aware service pruning (CASP) method. It manages QoS correlations by accounting for all services that may be integrated into optimal composite services and prunes services that are not the optimal candidate services. Our experiments show that this method can manage complicated correlations between services and significantly improve the QoS values of the generated composite services.