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 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.

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation.

In the real world, it can be challenging to annotate a large-scale dataset for all medical images, making few-shot medical image classification an important task. The latest advancements in pre-trained vision-language models as CLIP have demonstrated excellent performance in zero-shot natural image recognition and show advantages in medical applications. However, we have found that deploying such models in practical applications faces challenges in terms of engineering effort. It requires specialized medical domain knowledge and is time-consuming, as even slight variations in wording can have a significant impact on performance. Inspired by recent research on prompt learning in the field of Natural Language Processing (NLP), we propose a simple approach called Prompt Learner (PoLe) for automating the design of prompts in pre-trained vision-language models. This is a simple method specifically designed to fine-tune vision-language models, similar to CLIP, for downstream image recognition. In particular, PoLe models context tokens using continuous vectors that can automatically learn from medical images, thus avoiding the tedious process of handcrafting prompt engineering. Additionally, this approach maintains the frozen state of the large-scale pre-trained parameters, saving computational resources. Through extensive experiments on 5 medical image datasets, we have demonstrated that PoLe surpasses manually designed prompts with just one or two shots, and further training with more shots significantly improves the performance of image classification. For instance, when trained with 16 shots, the average improvement is approximately 20% (with a maximum improvement of over 31%). PoLe effectively transforms CLIP into a powerful few-shot learner. In terms of recognition performance, adjusting the CLIP model using PoLe yields better results than manually designed prompts for CLIP. When enhancing CLIP, PoLe demonstrates stronger learning capabilities compared to other few-shot learners such as linear probes. Furthermore, it outperforms CLIP models assisted by ChatGPT on most datasets. This indicates that PoLe possesses significant adaptability in the field of medical image analysis.

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 prompt tuning technologies and the emergence of prompt-as-a-service platforms, the security of prompt service has attracted the attention of researchers. Deep neural networks face susceptibility to various adversaries. A particularly malicious altering model behavior is backdoor attack, where model predictions exhibit divergence in the presence of specific triggers in inputs. We concentrate on backdoor attacks in code intelligence models. In this paper, we aim to propose a novel backdoor attack scenario target the security of prompt service. In traditional backdoor scenario, the victim model is fine-tuned on the downstream poisoned dataset to establish a shortcut between the trigger and the target label. This scenario suffers from flaws such as rare words inserted into code snippets resulting in alterations to the code’ s semantics. And only those attackers who are familiar with specific triggers can launch an attack. Instead of introducing extra rare words to code snippets, our attack scenario choose to employ prompt itself as the triggers. We evaluate our methods on three popular code intelligence tasks, including code defect detection, clone detection and code summarization. Experimental results indicate that our methods can achieve an attack success rate of 95% to 99% with almost no sacrifice in accuracy on the original task, exhibiting an average performance degradation of less than 1%. We hope exposed potential security risks hidden in prompt service can raise awareness among researchers.

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.

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.

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%.

Pre-trained language models (PLMs) have recently enabled rapid progress on sentiment classification under the pre-train and fine-tune paradigm, where the fine-tuning phase aims to transfer the factual knowledge learned by PLMs to sentiment classification. However, current fine-tuning methods ignore the risk that PLMs cause the problem of sentiment bias, that is, PLMs tend to inject positive or negative sentiment from the contextual information of certain entities (or aspects) into their word embeddings, leading them to establish spurious correlations with labels. In this paper, we propose an adaptive Gumbel-attacked classifier that immunes sentiment bias from an adversarial-attack perspective. Due to the complexity and diversity of sentiment bias, we construct multiple Gumbel-attack expert networks to generate various noises from mixed Gumbel distribution constrained by mutual information minimization, and design an adaptive training framework to synthesize complex noise by confidence-guided controlling the number of expert networks. Finally, we capture these noises that effectively simulate sentiment bias based on the feedback of the classifier, and then propose a multi-channel parameter updating algorithm to strengthen the classifier to recognize these noises by fusing the parameters between the classifier and each expert network. Experimental results illustrate that our method significantly reduced sentiment bias and improved the performance of sentiment classification.

Brain tumor segmentation is pivotal in the diagnosis and treatment of brain tumors. As functional imaging technologies like CT and MR advance, analyzing 3D medical image data becomes more time-consuming. Several challenges exist in 3D medical image segmentation: 1) 2D networks, when applied to 3D segmentation tasks, suffer from a lack of 3D structural information. 2) Pure 3D networks, due to their vast parameter count and smaller training sample, are susceptible to overfitting. 3) Current 2.5D networks do not fully leverage the available 3D structural information. In this study, we introduce the Mix-UNet, a multi-branch network that synergizes 2D and 3D networks. This design preserves essential 3D structural details for precise segmentation while ensuring computational efficiency. Our model comprises two main branches and a fusion module: a 2D branch for coarse segmentation without 3D structural information, a 3D branch to capture comprehensive 3D structural details, and a fusion module for pixel-level integration to produce the final segmentation. Experimental results demonstrate the model’s ability to reduce parameter count, increase robustness, and maintain high precision. When tested on the BraTS 2020 validation dataset, our model achieved mean dice coefficients of 90.4%, 80.7%, and 71.2% for the whole tumor, tumor core, and enhancing tumor, respectively, with only 2.2M parameters.

Although large language models (LLMs) have demonstrated impressive performance in code generation, they still face challenges when dealing with complex code generation tasks. In the software development process, humans often refine complex tasks iteratively and continuously modify and improve them. Inspired by this, we propose a self-iteration code generation framework based on large language models like ChatGPT. To realize this idea, we incorporated software development methodologies into the self-iteration framework. We introduced four roles into each cycle, including analyst, designer, developer, and tester. Each role performs different tasks during the self-iteration cycle, with analyst and designer continuously improving requirements analysis and task design based on the testing feedback provided by tester, while developer are responsible for refactoring or modifying code until it passes testing, concluding the entire self-iteration process. We conducted extensive experiments on multiple benchmarks. The experimental results indicate: (1) The code generated by the self-iteration framework achieves up to a 21.3% relative improvement in Pass@1 compared to direct code generation. (2) The self-iteration framework also exhibits strong generalization performance, enhancing code generation quality for different large language models."Failure is simply the opportunity to begin again, this time more intelligently."- Henry Ford

Regarding code generation, researchers have recently proposed a retrieve-template-generation approach. This method involves retrieving similar code snippets through a retriever and providing them to a generator along with input descriptions. However, since the retrieved similar code can be influenced by various data types, it may lead the model to reference unrelated content, resulting in some discrepancies between the generated code and the target code. To mitigate this bias, we introduce a code generation method based on retrieve-template-generation called RTCoder. Specifically, RTCoder completes code generation through three steps: Retrieve. Using a natural language description, the retriever retrieves several similar code snippets from a corpus. Template. By comparing these similar code snippets, it employs the Rabin-Karp algorithm to extract their common substrings and represents different substrings with spaces, forming a code template. Generator. The generator, based on a specific natural language description and the corresponding code template, automatically generates the concrete target code. We conducted extensive comparative experiments on three datasets and used three widely used evaluation metrics. The experimental results demonstrate that: (1) Compared to mainstream code generation models, RTCoder shows improvements in all three metrics across different datasets. For instance, compared to the state-of-the-art CodeT5 base, the EM value is 5.98%, 3.34%, and 1.67% higher on the three datasets, respectively. (2) Our approach is effective for other models as well. Taking the CodeBLEU score on the Concode dataset as an example, the retrieval-template-based generation method improved by 3.73% and 1.80% compared to direct generation and retrieval-generation methods on the RNN model, respectively.

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.

The conventional crowdsourcing system is dependent on a centralized platform, which grants the platform owner undue authority to manipulate the operation of the system for unethical profits. In this paper, the crowdsourcing system is revolutionized in a decentralized manner with Blockchain as a Service (BaaS). All crowdsourcing operations are implemented with smart contracts deployed on blockchain. Requesters invoke these contracts to publish tasks, while workers invoke them to submit solutions. Notably, the operation of assigning tasks is regarded as a crowdsourcing task to be performed by assigners. Multiple assigners compute respective task assignment schemes in an off-chain manner, and then submit the schemes to blockchain for competition. Performance evaluations show that not only the operating efficiency of the crowdsourcing system is improved, but also the adverse consequences of the system being maliciously manipulated are avoided. The proposed decentralized crowdsourcing system is anticipated to restructure the business model of the conventional crowdsourcing industry.

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.

Smart contracts are self-executing programs on the blockchain that are critical to a range of industries, including finance, supply chain management, and healthcare.However, comprehending smart contracts can be challenging due to a lack of effective comments in most user-defined code.To address this challenge, we propose a novel retrieval-enhanced approach CC-GRA that leverages retrieval knowledge to generate high-quality comments for Solidity language code.Our approach carefully eliminates duplicated data and template data in the widely-used smart contract dataset to ensure a high-quality corpus.Extensive experiments and comprehensive analysis demonstrate the effectiveness applicability of our approach after being compared with eight state-of-the-art baselines.Finally, we conduct a human study and find the comment quality generated by our approach is better than baselines in terms of similarity, naturalness, and informativeness.

Linjuan Wu, Shaojuan Wu, Xiaowang Zhang, Deyi Xiong, Shizhan Chen, Zhiqiang Zhuang, Zhiyong Feng. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Inferring gene regulatory networks based on single-cell transcriptomes is critical for systematically understanding cell-specific regulatory networks and discovering drug targets in tumor cells. Here we show that existing methods mainly perform co-expression analysis and apply the image-based model to deal with the non-euclidean scRNA-seq data, which may not reasonably handle the dropout problem and not fully take advantage of the validated gene regulatory topology. We propose a graph-based end-to-end deep learning model for GRN inference (GRNInfer) with the help of known regulatory relations through transductive learning. The robustness and superiority of the model are demonstrated by comparative experiments.

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.

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.

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.

Computational experiments are an important method for carrying out the quantitative analysis of complex systems and play a major role in mapping the real world to the virtual world. However, the flexibility of computational experiments leads to arbitrary modeling processes and unconvincing results, which greatly hinder the large-scale application of this method. In this context, the verification of computational models has become an urgent problem in this field. Currently, model evaluation is still in its infancy and the existing evaluation methods are not mature enough. Thus, we took epidemic models as the research object and proposed a capability maturity evaluation framework for computational models of artificial society. The framework differs from previous assessment methods that focus on the validity of results, but instead provides a comprehensive evaluation from two perspectives: 1) evaluation of the model itself—by comparing the expectation with the final implementation, we can obtain whether the model meets the expectation and 2) comparison between different models—by evaluating the implementation process of each model and comparing the results, we can identify more mature models. The implementation of the model is evaluated from input, process, and output. Further, specific analyses and evaluations are conducted for several representative COVID-19 models to verify the validity of this evaluation framework. The results of the case study show that the proposed evaluation framework can help decision-makers identify more mature and referential models, and point out the directions where modelers can improve their models.

Difficult samples of the minority class in imbalanced text classification are usually hard to be classified as they are embedded into an overlapping semantic region with the majority class.In this paper, we propose a Mutual Information constrained Semantically Oversampling framework (MISO) that can generate anchor instances to help the backbone network determine the re-embedding position of a non-overlapping representation for each difficult sample.MISO consists of (1) a semantic fusion module that learns entangled semantics among difficult and majority samples with an adaptive multi-head attention mechanism, (2) a mutual information loss that forces our model to learn new representations of entangled semantics in the non-overlapping region of the minority class, and (3) a coupled adversarial encoder-decoder that fine-tunes disentangled semantic representations to remain their correlations with the minority class, and then using these disentangled semantic representations to generate anchor instances for each difficult sample.Experiments on a variety of imbalanced text classification tasks demonstrate that anchor instances help classifiers achieve significant improvements over strong baselines.

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.

With the development of Online to Offline (O2O) model and the rapid growth of service types and numbers, service matching algorithms have become the key in connecting users and services. The traditional service matching algorithms lack consideration for the limited resources of O2O services, leading to the Matthew effect more seriously. In this context, how to alleviate the Matthew effect through the optimization of matching algorithms has become an urgent problem in this field. Based on this, this paper proposes an adaptive optimization algorithm of O2O service matching to achieve the balance of supply and demand by optimizing supply, thus alleviating the Matthew effect. In addition, a computational experiment system is constructed to verify the effect of different matching algorithms on alleviating the Matthew effect. The result shows that our proposed algorithm can provide new means and ideas for alleviating the Matthew effect.

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.

Summary Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self‐adaptation technologies for the service‐based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self‐adaptation and distributed knowledge‐based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self‐adaptation mechanism. We adopt the “survival of the fittest” principle for crucial elements in the ecosystem during evolution so that ultimately, service‐based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service‐based process are automatically repaired.

Most existing robust principal component analysis (PCA) and 2-D PCA (2DPCA) methods involving the l2-norm can mitigate the sensitivity to outliers in the domains of image analysis and pattern recognition. However, existing approaches neither preserve the structural information of data in the optimization objective nor have the robustness of generalized performance. To address the above problems, we propose two novel center-weight-based models, namely, centered PCA (C-PCA) and generalized centered 2DPCA with l2,p-norm minimization (GC-2DPCA), which are developed for vector- and matrix-based data, respectively. The C-PCA can preserve the structural information of data by measuring the similarity between the data points and can also retain the PCA's original desirable properties such as the rotational invariance. Furthermore, GC-2DPCA can learn efficient and robust projection matrices to suppress outliers by utilizing the variations between each row of the image matrix and employing power p of l2,1-norm. We also propose an efficient algorithm to solve the C-PCA model and an iterative optimization algorithm to solve the GC-2DPCA model, and we theoretically analyze their convergence properties. Experiments on three public databases show that our models yield significant improvements over the state-of-the-art PCA and 2DPCA approaches.

With the development of cloud manufacturing technology, Manufacturing Service Ecosystem (MSE) is emerging as a typical complex cyber-social system. On the one hand, service strategy (cyber layer) drives the evolution of manufacturing community (social layer); on the other hand, the initial conditions of manufacturing community (social layer) affect the performance of service strategy. In order to promote the evolution of MSE in the expected direction, it is necessary to clarify the loop feedback mechanism between heterogeneous networks. However, how to analyze and intervene in the possible evolution directions of MSE has become a serious challenge in the field. In order to face this challenge, this paper proposes a parallel system theory-based research framework to study the evolution and controlling of MSE. First, the corresponding digital system of MSE is constructed from the perspective of supply and demand matching. Second, the specific computational experiment is executed to present the effect of different service strategies (cyber layer) and different initial conditions (social layer) on the evolution of MSE. Furthermore, the comparison of experiment results with real data verifies the credibility of the proposed approach. It demonstrates that our approach can provide a new way for analyzing the complexity of MSE.

Imbalanced text classification, as practical and essential text classification, is the task to learn labels or categories for imbalanced text data. Existing imbalanced text classification approaches are mostly based on the Imbalance Ratio (i.e. ratio of sizes between categories). Recently, some researchers verified that the imbalance ratio severely affects the performance of classifiers when intrinsic characteristics of data such as class overlapping and small disjuncts occur. However, since the distribution of real-world data is unknown, it is difficult to describe above intrinsic characteristics directly. In this paper, we transform the unknown distribution of data into a graph model and present a graph-based imbalance index named GIR to predict the impact of imbalanced text data on classification performance. Firstly, we introduce an environmental factor that makes the imbalance index sensitive to the intrinsic characteristics of data. Secondly, we propose a graph-based method to calculate this environmental factor. Finally, we use the imbalance index to analyze the performances of imbalanced learning methods and the impact of imbalanced data on text classifiers. The experimental results evaluated on both synthetic data sets and real-world data sets demonstrate the effectiveness of our approach.

Emotional intelligence is a crucial part for human-machine dialogue system. However, the existing research on dialogue mainly faces three problems: (1) focus on the content level of each response while ignoring the impact of emotional factors in the multi-turn dialogue; (2) lacking scalability and adaptability is that only the emotion categories specified by users are generated in a single-turn dialogue; (3) it is difficult to capture and perceive fine-grained emotions and the speaker's emotional state according to the emotional context. To address these problems, we propose an emotional expression model in multi-turn dialogue (EmoEM), which combines emotion-semantic graph with multitask learning mechanism, applying the dialogue generator based on seq2seq network and graph convolution network (GCN) to generate more natural and personalized emotional responses in a structured manner. Generally, EmoEM considers constructing emotion-semantic graph to describe explicit and implicit emotions dynamically. Then, the emotion-semantic graph is applied to the dialogue generator based on the seq2seq neural network, mainly to improve the semantic consistency and text quality in multi-turn dialogue. Moreover, multi-task learning mechanism is introduced to enhance the emotional expression of the text and obtain expected emotional responses. The experimental results show that EmoEM outperforms several baselines in BLEU, diversity and emotional expression.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.

Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability . To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.

Crossover cooperation and fusion between services is becoming very common in the modern service industry. Crossover service fusion can create value that cannot be provided by single-domain services, thus achieving the value-emergence effect of 1+1>2. However, semantic inconsistencies in business and interface make crossover service fusion difficult and time-consuming. This paper proposes an interactive crossover service fusion approach based on microservice architecture to enable smooth and rapid integration of domain services. This approach takes service fusion requirements as the driving force to detect business inconsistencies between the services to be fused, and carries out business process reengineering by human-computer interaction. Then, semantic inconsistencies in service interface matching are detected and solved by splitting and completing parameter concepts to obtain the service fusion design scheme. Finally, the implementation scheme based on microservice architecture transforms the business coupling between domain services into asynchronous data communication, which facilitates the crossover fusion of complex business services. The elderly healthcare application is used to demonstrate and validate our approach.

In the context of big data, cloud storage services provide users with the ideal data storage service. But external store of data causes cloud storage service providers gain control of the data. Therefore, our work should consider how to ensure the privacy of data and maintain the integrity of data when enjoying convenient services. This paper builds a blockchain-based solution through research on cloud storage service model and blockchain technology. And related protocols are built on the solution-based architecture. In our solution, the decentralized model solves the single point of trust problem in the traditional data auditing service model by collective trust. A public agreement enables auditors to efficiently build proof of data integrity without touching data. The protocol allows users to trace the history of their data, and examine whether the owner of the data guarantees the privacy of the data in an after-the-fact audit. In addition, our work constructed the prototype system of the scheme and revealed the effectiveness of the scheme through system testing.

Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.

Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation-maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.

Semantic Annotation plays an essential role in automatic service discovery and composition. However, existing approaches and tools cannot achieve high annotation quality to ensure the semantic service application. Meanwhile, the semi-automatic strategies for improving the annotation quality are time-consuming. To further improve the efficiency as well as the quality of the annotation, this paper presents an effective method involving human-computer interaction to further optimize the annotation procedure. Besides employing the feedback and propagation strategy to semi-automatically improve the annotation quality, the strategy to involve the manual annotation is developed when the efficiency of semi-automatically strategy is related low. To optimize the manual annotation procedure, a clustering based approach is presented to select the most impacted candidates to optimize the annotation improvement. In addition, to help the annotators to choose the correct annotation, the local ontology restriction based method is further designed to improve the recommendation performance. The experiments show that our approach effectively involving the human intervention can significantly improve the annotation quality, faster the quality improvement procedure and reduce the manual load by increasing the recommendation accuracy.

With the popularity of cloud computing and micro service architectures, various service ecosystems including services, venders, and service-based processes continuously emerge on Internet or in an enterprise. Semantics of services from different venders may be described by distributed domain ontologies. Distributed knowledge brings difficulty to competition and cooperation among services, and hampers the evolution of a service ecosystem. In this paper, we propose a distributed knowledge based evolution model (DKEM) to promote competition and cooperation among services from different venders. DKEM considers stability as key factor in competition, and a stability evaluation model is designed to compute stability of services, venders, and service-based processes according to service invocation histories. Based on the evaluation model, two evolution patterns are given, and they can automatically explore new and more stable cooperation among services by means of runtime self-adaption mechanism. A prototype system for DKEM is implemented and a series of experiments show that DKEM is effective for competition and cooperation among services with distributed knowledge, and, evolved processes have higher stability and response efficiency.

With the advent of Web services, service interoperability has always been an active research issue. In recent years, many approaches have been proposed. However, how to achieve fast composition and guarantee correct and executable composite service remains an open issue. For this problem, this paper presents a three-phase framework for accurate and efficient service interoperability. Since service collaborations should follow certain constraints for success invocation, the goal of the first phase is to automatically clarify constraints on Web services. And then the second phase utilizes constraints acquired in previous phase to check Web services' constraint compatibility for accurate collaborations. In order to reduce the time on exhaustive analysis of service matching, the concept of expanded parameters is proposed, thus the problem of semantic matching is transformed into set operation. Subsequently, the third phase achieves interoperability among Web services by constructing initial composite services on the basis of collaborations, optimizing initial compositions to generate minimal composition alternatives with no redundant Web services, and executing final composition services. Experimental results show that our framework can dramatically reduce the time spending on service matching and effectively generate minimal composition alternatives in a rather short time.

Android system has been the crucial platform for the mobile service ecosystem. As a typical open source project, the release of the android system is a challenging issue because many developers are working on the related projects and it will affect millions of mobile service running on the platform. Therefore, investigating the release process of Android system is important for the mobile service ecosystem. Particularly, in this paper, we will focus on the release features prediction issue of what features should be included in the new publishing version. The valid changes and release notes are transformed into low-dimensional vectors and then the automatic labelling methodology is developed to detect the features. Combing with the time series forecasting model, an approach to predict the published features in the new version is presenting. Based on the data collected from the Android Open Source Project (AOSP), the experiments show that: comparing with the state-of-the-art, our approach achieves 13.83% to 17.69% precision improvement in releasing feature predictions and we can effectively detect the spike features for further compatibility management.

Community detection has been one of the key research topics in the analysis of networked data, which is a powerful tool for understanding organizational structures of complex networks. One major challenge in community detection is to analyze community structures for streaming networks in real-time in which changes arrive sequentially and frequently. The existing incremental algorithms are often designed for edge-grained sequential changes, which are sensitive to the processing sequence of edges. However, there exist many real-world net-works that changes occur on node-grained, i.e., node with its connecting edges is added into network simultaneously and all edges arrive at the same time. In this paper, we propose a novel incremental community detection method based on modularity optimization for node-grained streaming networks. This method takes one vertex and its connecting edges as a processing unit, and equally treats edges involved by same node. Our algorithm is evaluated on a set of real-world networks, and is compared with several representative incremental and non-incremental algorithms. The experimental results show that our method is highly effective for discovering communities in an incremental way. In addition, our algorithm even got better results than Louvain method (the famous modularity optimization algorithm using global information) in some test networks, e.g., citation networks, which are more likely to be node-grained. This may further indicate the significance of the node-grained incremental algorithms.

Semantic annotation plays an important role for semantic-aware web service discovery, recommendation and composition. In recent years, many approaches and tools have emerged to assist in semantic annotation creation and analysis. However, the Quality of Semantic Annotation (QoSA) is largely overlooked despite of its significant impact on the effectiveness of semantic-aware solutions. Moreover, improving the QoSA is time-consuming and requires significant domain knowledge. Therefore, how to verify and improve the QoSA has become a critical issue for semantic web services. In order to facilitate this process, this paper presents a novel lifecycle framework aiming at QoSA assessment and optimization. The QoSA is formally defined as the success rate of web service invocations, associated with a verification framework. Based on a local instance repository constructed from the execution information of the invocations, a two-layer optimization method including a local-feedback strategy and a global-feedback one is proposed to improve the QoSA. Experiments on real-world web services show that our framework can gain 65.95%~148.16% improvement in QoSA, compared with the original annotation without optimization.

Loosely coupled and cross-platform features make Web services accessible and increasingly popular on the Internet. However, efficient service discovery and automated service composition are still challenges under the conventional practice where services are organized into categories. In this paper, we propose a graph-based method to organize Web services into a service ecosystem interlaced with service relationships at the semantic level. First, Web services are modelled as a set of interfaces, whose input and output parameters are annotated with well-defined ontologies. Secondly, semantic associations and interactions between Web services are mined, and services are constructed into a Web services network (SN), a variant of bipartite graph, by projecting the functional aspects of concrete Web services onto the abstract service layer. Thirdly, from the complex network perspective, the services relations are investigated and the structure of SN is analysed. To demonstrate the basic topological properties of SN, an empirical study is conducted on two data sets for comparative purposes, 10 000+ Web services collected from the Internet and 1231 Web services provided by Titan system of Zhejiang University. The experimental results reveal that SNs, which are built by different data sets on the semantic level, exhibit the same features such as small-world and scale-free. In addition, our results yield valuable insight for developing service discovery and automated composition algorithms, and characterizing the evolution of the entire Web service ecosystem.

Service relations facilitate the automation of service reuse. Most of studies on the service relations focus on the inputs and outputs. However, different Web Services tend to utilize the same parameters without formally specifying their constraints. Due to this, the semantics, introduced by semantic annotation, is still not rich enough for accurate descriptions, thus generating a large number of inappropriate service relations. To address this, we propose an approach for augmenting semantics of Web Services based on constraints, which can be regarded as a complement to semantic annotation. The semantics is augmented via a hybrid analysis of heterogeneous constraints, including the server constraint and object constraint.

Social annotation provides a convenient way to annotate shared content by allowing users to use any tag or keyword. While free folksonomy is widely used in social software implementations and especially in web services, it will play an important role in the semantic web services. However, such tags cannot offer the expressivity of ontologies, and the respective tags often lack context-independent and explicit semantic. In this paper, we describe a model to extract domain concept from social tags. The model mainly includes three modules: a) Detecting the noun terminology through mutual information, b) Applying semantic dictionary to disambiguate between tags, c) Filtering the domain concept via domain relevance and consensus. Finally, experimental results on real world data sets show that the model can effectively learn the domain concept from social tags, and the concept also has a high degree of generality and applicability.