With growing concerns over user privacy, federated recommendation (FedRec) has emerged as a mainstream solution for personalized recommendation services. FedRec trains user-private parameters on local clients while collaboratively updating global parameters on a centralized server. However, despite advances in optimizing these local and global parameters, existing methods overlook two key challenges: tradeoff training and distribution discrepancy . Tradeoff training balances timely local updates with diverse global parameters, limiting the model’s learning ability. Distribution discrepancy arises from the divergence between locally trained global parameters and those aggregated by the server, corrupting inference performance. To fill in the gap, we propose FedSC , a principled federated recommendation framework that boosts FedRec’s training and inference processes with minimal yet nontrivial efforts. During training, FedSC employs a stochastic aggregation strategy where all users participate in every round, while only a random subset is selected for aggregation, preserving the diversity of global parameters and ensuring timely local updates. During inference, FedSC makes recommendations with a consistency inference mechanism that uses the most recent locally trained global parameters of each user to improve the model’s understanding of user preferences. Extensive experiments on multiple benchmark datasets demonstrate the superiority of FedSC, achieving up to a 20% improvement in most evaluation scenarios.

Existing causal learning algorithms focus on micro-level causal discovery, confronting significant challenges in identifying the influence of macro systems, composed of micro-level variables, on other variables. This difficulty arises because the causal relationships in macro systems are often mediated through micro-level causal interactions, which can lead to erroneous causal discovery or omission when dispersed. To address this issue, we propose the Emergence-inspired Multi-granularity Causal learning (EMCausal) method. Inspired by the emerging phenomena of aggregating micro level variables into macro level representations, EMCausal introduces a progressive mapping encoder to simulate the process, thus capturing the causal relationships driven by these macro entities. Next, it introduces a causal consistency constraint to collaboratively reconstruct micro variables using macro-level representations, enabling the learning of a multi-granular causal structure. Experimental results on both synthetic and real datasets demonstrate that EMCausal can identify causal graphs under the influence of causal emergence, outperforming competitive baselines in term of accuracy and robustness.

Due to the distributed nature of federated learning (FL), the vulnerability of the global model and the need for coordination among many client devices pose significant challenges. As a promising decentralized, scalable and secure solution, blockchain-based FL methods have attracted widespread attention in recent years. However, traditional consensus mechanisms designed for Proof of Work (PoW) similar to blockchain incur substantial resource consumption and compromise the efficiency of FL, particularly when participating devices are wireless and resource-limited. To address asynchronous client participation and data heterogeneity in FL, while limiting the additional resource overhead introduced by blockchain, we propose the Directed Acyclic Graph-based Asynchronous Federated Learning (DAG-AFL) framework. We develop a tip selection algorithm that considers temporal freshness, node reachability and model accuracy, with a DAG-based trusted verification strategy. Extensive experiments on 3 benchmarking datasets against eight state- of-the-art approaches demonstrate thatDAG-AFL significantly improves training efficiency and model accuracy by 22.7% and 6.5% on average, respectively.

Federated Recommendation (FedRec) systems are recognized as privacy-preserving solutions for collaboratively training recommender models without sharing users’ private data. However, recent studies have revealed that FedRec systems are vulnerable to interaction-level membership inference attacks. In such attacks, a semi-honest server can employ crafted methods to infer users’ interacted items. In this article, we identify that user preference information is predominantly stored in the user-uploaded parameters rather than in the local parameters after local training. Leveraging this insight, we expose a new interaction vulnerability and introduce the PubPara attack. Our experiments show that PubPara improves the inference performance by at least 40% over existing attacks, while requiring minimal inference time and remaining robust against current defense methods. To safeguard user privacy without compromising recommender performance, we propose MultiVerse, a novel countermeasure. MultiVerse utilizes untrained items outside the user’s local training data to obfuscate the server’s inference of interacted items. It includes a four-step strategy (training, optimization, refinement, and denoising) to achieve robust defense. Extensive experiments on three representative FedRec models (F-NCF, F-LightGCN, and FedRAP) across three real-world datasets validate that MultiVerse significantly degrades the attack’s inference performance to near the level of random guess while maintaining lossless recommender performance.

More and more business applications are crossing organization boundaries and typically involves a set of interactive organizations, known as cross-organization business process management. By taking as input the distributed event logs of each organization, cross-organization process mining techniques can reconstruct the underlying business process model to help process comprehension and improvements. Unfortunately, existing process mining techniques completely ignore the privacy issue, i.e., the privacy of the event log and business process model is not guaranteed. To cope with this challenge, this paper proposes a privacy-preserving cross-organizational business process mining framework based on blockchain and cryptography. Specifically, it mainly includes three steps: (1) each organization builds its private business process model, interaction messages, and collaborative tasks from its event log; (2) collaborative public process model for each organization is generated based on blockchain using privacy security intersection (PSI) cryptography algorithms to ensure the privacy of each organization; and (3) each organization combines its private business process model with relevant public process models, to obtain an organization-specific collaborative business process model. Using four public cross-organization datasets, the privacy-preserving ability and application of the proposed technique is demonstrated.

Abstract Similarity computation is a fundamental aspect of information network analysis, underpinning many research tasks including information retrieval, clustering, and recommendation systems. General SimRank (GSR), an extension of the well-known SimRank algorithm, effectively computes link-based global similarities incorporating semantic logic within heterogeneous information networks (HINs). However, GSR inherits the recursive nature of SimRank, making it computationally expensive to achieve convergence through iterative processes. While numerous rapid computation methods exist for SimRank, their direct application to GSR is impeded by differences in their underlying equations. To accelerate GSR computation, we introduce a novel approach based on linear systems. Specifically, we transform the pairwise surfer model of GSR on HINs into a new random walk model on a node-pair graph, establishing an equivalent linear system for GSR. We then develop a fast algorithm utilizing the local push technique to compute all-pair GSR scores with guaranteed accuracy. Additionally, we adapt the local push method for dynamic HINs and introduce a corresponding incremental algorithm. Experimental results on various real datasets demonstrate that our algorithms significantly outperform the traditional power method in both static and dynamic HIN contexts.

A recent trend of fair machine learning is to build a decision model subjected to causality-based fairness requirements, which concern with the causality between sensitive attributes and decisions. Almost all (if not all) solutions focus on a single fair decision model and assume no hidden confounder to model causal effects in a too simplified way. However, multiple interdependent decision models are actually used and discrimination may transmit among them. The hidden confounder is another inescapable fact and causal effects cannot be computed from observational data in the unidentifiable situation. To address these problems, we propose a method called CMFL (Causality-based Multiple Fairness Learning). CMFL parameterizes the causal model by response-function variables, whose distributions capture the randomness of causal models. CMFL treats each classifier as a soft intervention to infer the post-intervention distribution, and combines the fairness constraints with the classification loss to train multiple decision classifiers. In this way, all classifiers can make approximately fair decisions. Experiments on synthetic and benchmark datasets confirm its effectiveness, the response-function variables can deal with the unidentifiable issue and hidden confounders.

Major depressive disorder (MDD) is the most common psychological disorder that affects mental and physical health. To narrow the gap in real world mental healthcare and improve the effectiveness of MDD treatment, an increasing number of artificial intelligence (AI) methods have been proposed to explore electroencephalography (EEG) features, including traditional signal features and measures of brain functional connectivity network (BFCN), for the recognition of depression-related patterns. However, these methods fail to capture long-term dependencies and limit the modeling ability of information transmission dependencies in MDD brain regions. To address these issues, we propose a novel brain functional residual temporal convolution network (BFRTCN) method for MDD recognition. On one hand, this model directly focuses on the connectivity weights of BFCNs to model the information transmission between brain regions, allowing for better differentiation of the differences in information transmission patterns between MDD and normal control (NC). On the other hand, we introduce a residual temporal convolution network (ResiTCN) that utilizes temporal convolution layers to capture short-term changes in brain regions and establish residual connections to help maintain long-term dependencies for improving ability to capture disease variations. Experimental results on benchmark datasets validate the superior performance and time complexity of BFRTCN. Analysis shows that the Beta band MDD transmission mode is relatively stable. There are defects in the brain functional connections between the frontal and right temporal (RT) regions on Alpha and Gamma bands, which can serve as potential biomarkers for MDD recognition.

Addressing untrustworthy behavior in cross departmental business process collaboration is the focus of current research. The untrustworthiness of the business process not only leads to the misuse and leakage of business data, but also leads to cheating by the participants in the business process. This paper proposes a blockchain-based trustworthy cross-departmental business process collaboration service mechanism(TBPCS) to solve the above problems. This paper firstly maps the participants, data and data ownership of the business process to the blockchain, which prevents the business process from being tampered with. This method allows the participants in the business process to act under the constraints of a trusted environment, avoiding the illegal use of business data. Then this paper maps the business process to the blockchain in the form of smart contracts, which ensures crossdepartment business process collaboration is trustworthy and reduces the verification cost of business data. This paper innovatively proposes a rollback mechanism that supports business interruptions. The mechanism ensures that the business process is trustworthy even in abnormal situations.

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

Mining Electronic Health Records (EHRs) is of great significance to improve the efficiency and quality of medical services. In recent years, researchers have used deep recurrent neural networks to predict patients' next-period prescriptions. The main challenges of predicting next-period prescriptions are as follows: i) The latent interdependence information which can be utilized to enhance the representation of the data between heterogeneous features is dynamic. However, most existing approaches do not consider capturing and utilizing this information. ii) Conventional recurrent neural networks cannot store historical interdependent information between heterogeneous features and take advantage of it. To address these challenges, We propose a novel attention mechanism named Cross Attention (CA) that can capture the interdependence between two sequences. We further propose three types of recurrent neural networks that can capture and utilize current and historical latent interdependence between the two sequences. Extensive experiments on real-world data demonstrate that DSCA networks can capture the interdependence information between sequences and outperform state-of-the-art methods.

In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well. This dual objective can be summarized by the concept of productive laziness. Existing scheduling approaches mostly focus on efficiency but overlook worker wellbeing through proper rest. In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper. It intelligently recommends personalized work-rest schedules based on local data concerning a worker's capabilities and situational factors to incorporate opportunistic resting and achieve superlinear collective productivity without the need for explicit coordination messages. Extensive experiments based on a real-world dataset of over 5,000 workers demonstrate that CPL enables workers to spend 70% of the effort to complete 90% of the tasks on average, providing more ethically aligned scheduling than existing approaches.

Diabetes is the 5thleading cause of death in the world, which threatens people's physical and mental health. However, in the course of studying diabetes, the missing medical data has caused great trouble for researchers. There are few researches focusing on sparse medical data. Common method of filling missing values is not desirable. In this study, we select and analyze a large physical check dataset containing more than 900 records with missing data. Using these data, a model for predicting diabetes is designed and implemented. Before predicting, logistic regression is utilized to remain the most significant attributes as the input variables of prediction model. Concept lattice is considered an effective mathematical tool for conceptual data analysis and knowledge processing in mathematics. Experimental results demonstrate that concept lattice illustrates great capacity of predicting diabetes while remaining missing data.

In real-world, name ambiguity mainly arises when many people share the same name or express their names in the same way, which often causes erroneous aggregation of records of multiple persons with the same name. This name ambiguity problem deteriorates the performance of information retrieval in digital libraries, web search etc. It is nontrivial to distinguish those name references, especially when there is very limited information about them. Most existing studies uses features like email address, frequent words etc. However, the information is not always available because of privacy or too expensive to get. In this paper, we utilize a graph node embedding approach to solve author name disambiguation problem, where a graph is constructed only using the collaborator relationships. In the methodological aspect, the proposed method uses random walk and a graph node representation learning method to embed each node into a low dimensional vector space. Finally, we solve this problem by partitioning the records associated with a name reference such that each partition contains records pertaining to a unique real-world person. We evaluate our method on the real world CiteSeerX dataset, and the experimental results demonstrate that the proposed method is significantly better than most of the existing name disambiguation methods working in a similar setting.

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service workflows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2,000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

With the development of modern society, business processes are widely used, and they play an important role in peo-ple's daily life. In government agencies, there are many business processes employed to deal with different kinds of transactions. Such as one of the medical insurance refund processes is used to claim for maternity insurance. In order to analyze, evaluate and optimize the business process in different situations, this paper develops to use the queueing network theory to establish the corresponding business process model. Then apply this model to analyze the performance of the business process by using four performance metrics i.e., average queue length, average job sojourn time, job arrival intensity and throughput. Finally, this paper optimizes the business process based on three situations. It has been proven that the optimization methods are scientific and the business process after the optimization is more efficient.

The rapidly increasing availability of healthcare data from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, and patient management. The patient healthcare data are usually longitudinal and can be expressed as medical event sequences, where the events include clinical diagnosis, medications, laboratory reports, etc. Because healthcare data has both longitudinal and heterogeneous attributes, analyzing healthcare data is an inherently difficult challenge. In this paper, we propose a hospital readmission prediction method using temporal phenotypes, namely the Tephe. Specifically, each patient's medical event sequence is first represented by a temporal graph, which captures temporal relationships of the medical events in each event sequence and makes the raw data more intuitive. Based on graph pattern mining, we define more significant frequent subgraphs as temporal phenotypes. This enables us to better understand the disease evolving patterns and treatment approach. In addition, we designed an improved greedy algorithm to find the optimal expression coefficient of frequent subgraphs for each patient. Finally, based on the optimal expression coefficient of the frequent subgraph, random forests are used to perform prediction tasks. The experimental results show that our proposed method is more accurate in the prediction tasks compared with the baselines.

Evaluation of the Deep Web data sources must be based on the data in the Web databases, then how to select the most representative keywords as a query word to obtain a large number of uniformly distributed data is a major difficulty, this paper proposed a Deep Web database sampling method based on high correlation keyword, using a graph based keyword-connected network to get query words, the method can get a random sample of high-quality data from the Deep Web data source more efficiently.

In cloud computing, it is an urgent problem to provide stable composition service which can satisfy the personalized requirements for large scale users. This paper takes several aspects of web service into consideration, including Quality of Service (QoS), user preference and the service relationships and proposes a method based viterbi algorithm to reason out the global optimal solution of web composition service. Result shows our method holds executive efficiency, stability as well as outstanding selecting result.

In order to support workflow-oriented application on multiple data centers, this paper describes a workflow-oriented cloud computing framework, called WfOC. WfOC can run workflow jobs composed of multiple user defined task-functions extracted by java annotation. This framework includes workflow-oriented cloud computing programming language, tasks extraction and composition, tasks and data sources registration, tasks functions mapper/reducer and other components, and enables users to especially focus on workflow definition and workflow tasks logic implementation without needing to worry about the distribution of data and target execution systems. A mechanism is offered in building workflow-oriented data intensive applications, with multiple heterogeneous java runtime environments as the underlying computation platform. A case in social security application on multiple databases shows this framework can streamline complex computational workflow.

In order to implement the complex business process involved in multi-computer applications, it is necessary for correlative computer applications to cooperate with each other and connect to each other. Under the traditional client/server architecture, there are many difficulties in implementing connections to each application. It can offer a better foundation of architecture that constructs an application server and forms the multi-tier architecture of the client/application server/database server using a component based software technique. However, this architecture needs the correlative applications to connect to each other continuously. Apparently, it is confined under the environment of noncontinuous connection. In this paper we propose the concept of offline component agent and the multi-tiered architecture based on an offline component agent, which can effectually implement interconnection of multi-applications under the environment of non-continuous connection. Offline component agents provided by the server application and configured at the client application process business logic and data logic in correlative server applications. The client application and offline component agent maintain continuous connection, but the offline component agent and server application may not maintain continuous connection, these two parts cooperating with each other according to special arithmetic. The merit of the architecture of software multi-tier components includes clarity of the interface between different applications, consistency between software structure and problem structure, better encapsulation of software logic, and the advantages of safe management and simplicity of maintenance and version management. We explicate the architecture based on the offline component agent.