The non-cooperative game between rational vehicle users will generate unnecessary costs, manifested as the gap between user equilibrium (UE) and system optimal (SO). This issue primarily arises due to the competition or the untrusted collaboration among vehicles. The traditional marginal cost pricing (MCP) method is constrained by factors such as vehicle density and communication protocols, resulting in suboptimal performance. In this paper, the immutability of Hashgraph is leveraged to enable trusted services in the Internet of Vehicles (IoV), transforming non-cooperative games into a global optimization problem, while a two-phase optimization method is proposed to achieve low-cost services. Firstly, this paper simplifies the process of determining consensus timestamps for Hashgraph and constrains the actions of participants through immutability, thereby guaranteeing trusted services more efficiently. Subsequently, this paper systematically analyzes the key factors affecting travel and network service costs to optimize them in turn. Specifically, regarding the travel costs, this paper introduces a segment shielding method in trusted scenarios to avoid Braess’s paradox. As for the network service costs of data sharing, this paper presents a latency-sensitive dynamic programming method to integrate each server’s status to optimize resource scheduling. When the vehicle density is 400, the proposed method reduces the total cost by 20.920% and improves the QoE by 122.535%. The advantages become more significant as vehicle density increases.

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.

With the rapid development of the Internet of Vehicles (IoV), vehicles will generate massive data and computation demands, necessitating computation offloading at the edge. However, existing research faces challenges in efficiency and trust. In this paper, we explore the IoV computation offloading from both user and edge facility provider perspectives, working to optimize the quality of experience (QoE), load balancing, and success rate based on challenges to efficiency and trust. First, two vehicle interconnection models are constructed to extend the linkable range of intra-road and inter-road vehicles while considering the maximum link time constraint. Then, a dynamic planning method is proposed, combining the reputation and feedback mechanisms, which can schedule edge resources online based on the cumulative computation latency of each service side, reliability value, and historical behavior. These two phases further improve the efficiency of edge services. Subsequently, blockchain is combined to optimize the trust problem of edge collaboration, and an edge-limited Byzantine fault tolerance local consensus mechanism is proposed to optimize consensus efficiency and ensure the reliability of edge services. Finally, this paper conducts dynamic experiments on real-world datasets, verifying the effectiveness of the proposed algorithm and models in multiple vehicle density datasets and experimental scenarios.

With the development of 5G technology, ultradense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bring new energy consumption problems. In this article, we explore the energy consumption of the edge demand response (EDR) from the two perspectives of edge users and edge facility providers. While guaranteeing the essential Quality of Experience (QoE), we try to improve the load balancing of edge servers and reduce system energy consumption. On the edge facility provider side, we mainly consider the physical machine turn-on problem and resource allocation of the two-phase EDR. On the user side, we start with user cost reduction and subchannel power allocation to emphasize the QoE. First, considering the density impacts of users and edge servers, we take inspiration from the classical PageRank algorithm and propose a method to calculate the edge nodes’ weights and, thus, determine the infrastructure’s state. Subsequently, combining distance factors, we design a subchannel power allocation method based on dynamic planning for 5G power-domain multiplexing nonorthogonal multiple access (PDM-NOMA). More importantly, based on the above work, we optimize the two-phase EDR process based on the upper confidence bound (UCB) algorithm of the multiarmed bandit (MAB) algorithm framework and dynamic planning. We compare the proposed QEL-UCB algorithm with two classical and five state-of-the-art algorithms on a real-world data set. The experimental results demonstrate that the proposed method improves by 18.52% in load balancing and reduces by 18.59% in energy consumption, which validates the method’s effectiveness.

Abstract The health of mobile software ecosystems is closely related to the interests of software developers, end‐users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems.

Governance strategies related to platform ecosystems have become a vital issue for developing a smart society, attracting governments’ and practitioners’ attention. Under the consensus of “service as a commodity” and “platform as market,” service providers, platforms, services, and various supply demand matching methods form new supply processes. These elements are continuously and uncertainly changing during supply demand matching, which makes platform ecosystems constantly evolving. However, when multiple supply demand matching methods coexist such as service composition and crossover fusion, dynamic service supply and matching cause dilemmas in the platform ecosystem governance. To this end, this article proposes a model for platform ecosystem evolution with four dynamics: 1) dynamics between ISPs (services) and platforms; 2) dynamics between users and platforms; 3) dynamics among services; and 4) dynamics between services and demands. The model considers multiple supply demand matching methods and considers both fully online services and incompletely online services. Then, according to the market operation law, we design six evaluation indexes such as demand matching rate, service diversity, and market concentration to evaluate the efficiency of the platform market. Finally, a computational experiment system is established to simulate the dynamic supply and matching processes. The experimental results show that reducing the cost of service release can increase the amount of demand and the diversity of services, and the monopoly of digital platforms is a natural trend to improve the efficiency of supply and demand. The model provides a reference for the governance of platform ecosystems and lays a foundation for further research on the value cocreation mechanism of platform ecosystems.

Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results in performance decreases when confronted with cross-domain scenarios including domain- or project-specific situations. This decline can be attributed to their limited ability to effectively capture the semantics associated with such scenarios. To tackle the aforementioned problem, we propose a ze R o-shot dom A in ada P tion with pre-tra I ned mo D els framework for code search named RAPID. The framework first generates synthetic data by pseudo labeling, then trains the CodeBERT with sampled synthetic data. To avoid the influence of noisy synthetic data and enhance the model performance, we propose a mixture sampling strategy to obtain hard negative samples during training. Specifically, the mixture sampling strategy considers both relevancy and diversity to select the data that are hard to be distinguished by the models. To validate the effectiveness of our approach in zero-shot settings, we conduct extensive experiments and find that RAPID outperforms the CoCoSoDa and UniXcoder model by an average of 15.7% and 10%, respectively, as measured by the MRR metric. When trained on full data, our approach results in an average improvement of 7.5% under the MRR metric using CodeBERT. We observe that as the model’s performance in zero-shot tasks improves, the impact of hard negatives diminishes. Our observation also indicates that fine-tuning CodeT5 for generating pseudo labels can enhance the performance of the code search model, and using only 100-shot samples can yield comparable results to the supervised baseline. Furthermore, we evaluate the effectiveness of RAPID in real-world code search tasks in three GitHub projects through both human and automated assessments. Our findings reveal RAPID exhibits superior performance, e.g., an average improvement of 18% under the MRR metric over the top-performing model.

In recent years, applying smart contract to Blockchain-based Service Networks (BSNs) has been considered as one of the most promising solution to boost the integration and adoption of Blockchain in big businesses. However, smart contract are especially vulnerable to attack due to poor coding. Although many existing vulnerability detection tools are restricted by rigorous rules that are defined by the experts in advance, these tools are observed to have a high false positive rate in practice. Thus we propose a vulnerability detection framework for smart contract based on the attention mechanism and data flow. The code of smart contract is transformed to a data flow according to the abstract syntax tree that is built from the code. The data flow we built with smart contract code could represent the relationships of code semantic logic. Source code, data flow, and the tags of smart contract code are used as datasets to mask processing. Then, we construct a bidirectional multi-layer transformer architecture based on the attention mechanism to train our dataset. After training, we can get the label of whether there is a vulnerability in the final smart contract. Finally, the model we proposed reaches state-of-the-art results in the practical experiments of smart contract vulnerability detection with 92.54%, 81.79%, and 86.84% in the results Accuracy, Recall, and F1score, respectively.

The incompleteness of android documentation causes the lack of permission semantics, which in turn leads to permission misuse and threatens android system security. Previous studies have used API information to supplement permission semantics and recommended permissions, but they still lack sufficient contextual information, e.g. category and user review grades. To solve this problem, a novel Knowledge Graph based Convolutional Propagation Model (KGCP) is proposed for apps permission recommendation. In KGCP, we construct a knowledge graph (KG) to model the contextual information corresponding to apps and permissions. In order to regularize the representations of items, KGCP utilizes KG embedding technique to preserve its intrinsic structure while embedding entities and relationships into a continuous vector space. Focused on apps permission recommendation, KGCP learns the representation of entity through graph convolutional networks, which recursively aggregates information about neighbors in KG to mine potential preferences for app entities over permission entities. Experimental results show that KGCP improves by 16.7% over state-of-the-art apps permission recommendation methods, thus helps developers find the suitable permissions faster and more accurately.

Neural Networks (NNs) have received extensive attention and research due to their ability to extract and combine different features non-manually and to mine the internal relationships between features. In quantum mechanics, the entangled state simultaneously describes the classical and non-classical correlation between subsystems, so it can reveal important quantum phenomena such as non-locality, entanglement, etc., but its essence is the characterization of strong statistical relationships. Considering the superiority of the entangled state, this article attempts to use the entangled state to reconstruct the neurons of NNs to achieve that the network model can characterize the strong statistical relationship between the features, namely, the classical and non-classical correlation. Specifically, based on concurrence, a quantification method of entanglement, we propose a regularizer that can constrain a state vector to an entangled state, and apply it to the optimization process to ensure that the vector passed to the neuron is a legal entangled state. Finally, the entangled state is measured to obtain the output of the neuron. Experimental results show that our model is better than the baseline. Moreover, it performs well compared to the model inspired by quantum entanglement.

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

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.

QoS prediction is the most important step in web service selection and service recommendation. In the field of service computing, the most frequently method that adopted to predict the web service QoS is collaborative filtering. The existing studies usually focus on the prediction of the exact QoS values, and never prediction about its fluctuation range which is caused by the random dynamic nature of the internet and web server payload. In fact, the web service QoS(such as response time, throughput, etc.)invoked by clients are dynamic range, they are not a exactly value. In this paper with the help of non-parametric statistical bootstrap technique, we proposed a approach to predict the confidence interval of web services QoS and then compared the performance of web services according to the method. In the first phase, we take use of the the method of Shapiro-Wilk normal test to analysis the distribution of web services QoS(response time) and use the method of non parametric hypothesis test kruskal-Wallis to analysis the dynamic nature of interval respectively. In the second phase we adopted the bootstrap techniques in Nonparametric statistical and collaborative filtering algorithm to propose a method which could predict the confidence interval of web service response time when the client invoking the web service, and then compared the performance of web services accordingly.