With the rapid development of scientific research, a large amount of literature materials (e.g., published papers) has brought a serious information overload problem to researchers. For those novices who have just stepped into a certain research field, the fact that they do not yet know their own research direction, coupled with the huge amount of literature materials and their varying quality, makes it even more difficult for them to retrieve high-quality papers. To this end, we propose a Cross-community academic Knowledge Graph based approach for machine learning paper Recommendation (CKGR). Considering the hierarchical structure of cross-community knowledge graphs, we utilize knowledge propagation in hyperbolic space for entity representation learning. To alleviate the data sparsity problem as well as to learn better entity representations, we further introduce a preference migration module and contrastive learning. Meanwhile, considering the semantic relationships among entities, we introduce textual information to enhance the connection among interacting nodes for better recommendation tasks.

Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy. Group fairness and client fairness are two dimensions of fairness that are important for FL. Standard FL can result in disproportionate disadvantages for certain clients, and it still faces the challenge of treating different groups equitably in a population. The problem of privately training fair FL models without compromising the generalization capability of disadvantaged clients remains open. In this paper, we propose a method, called mFairFL, to address this problem and achieve group fairness and client fairness simultaneously. mFairFL leverages differential multipliers to construct an optimization objective for empirical risk minimization with fairness constraints. Before aggregating locally trained models, it first detects conflicts among their gradients, and then iteratively curates the direction and magnitude of gradients to mitigate these conflicts. Theoretical analysis proves mFairFL facilitates the fairness in model development. The experimental evaluations based on three benchmark datasets show significant advantages of mFairFL compared to seven state-of-the-art baselines.

Crowdsourcing is a promising human-in-the-loop paradigm for processing computer hard tasks by harnessing crowd intelligence. However, canonical crowdsourcing systems mostly need to aggregate/transmit worker data and may lead to privacy-leakage. To tackle this problem, we propose a novel approach, called FedCS (Federated CrowdSourcing), to achieve privacy protection while ensuring quality. FedCS aggregates model parameters from clients to build a shared server model while keeping the training data locally on worker devices to protect data privacy. To mitigate the staleness of stragglers and boost efficiency, we introduce a semi-asynchronous federated crowdsourcing mechanism, where the parameter server performs global aggregation periodically. Moreover, due to the different frequencies of workers participating in asynchronous update, FedCS uses a staleness-aware grouping and weighted aggregation heuristic to balance the training process. To speed up the convergence rate and improve the training accuracy, FedCS deploys adaptive learning step size for worker devices by their participation frequency. We further present a task assignment algorithm to help workers choose worthy and suitable tasks for annotations and to save the budget. Extensive experiments on benchmark datasets and a real-world crowdsourcing project show that FedCS can complete secure crowdsourcing projects with high quality and low budget.

Text-to-SQL aims to parse natural language problems into SQL queries, which can provide a simple interface to access large databases enabling SQL novices a quicker entry into databases. As the Text-to-SQL field is intensively studied, more and more models use GNNs to encode heterogeneous graph information in this task, and how to better obtain path information between nodes in database schema heterogeneous graphs and question-database schema heterogeneous graphs will greatly affect the effectiveness of the model parsing. Our work intends to explore the problem of solving the encoding of heterogeneous graph meta-paths in the Text-to-SQL task. Previous approaches usually use multi-layer GNNs to aggregate topological structure information between nodes. However, they ignored the structural information embedded at the edges and also failed to obtain nodes that are not directly connected but can provide contextual information through meta-paths. To solve the above problem, we propose Multi-Hop Relational Graph Attention Network based Text-to-SQL Parsing Model (MHRGATSQL) for learning topological information between nodes while obtaining semantic information embedded in the edge topology. We use multi-hop attention to modify the relational graph attention network to diffuse the attention scores throughout the network, thus increasing the “receptive field” of each layer of RGAT. Experimental results on the large-scale cross-domain Text-to-SQL dataset Spider show that our model obtains an absolute improvement of 1.7% compared to the baseline and alleviates the over-smoothing problem in the deep network model.

Knowledge graph (KG) has been widely used in the field of recommender systems. There are some nodes in KG that guide the occurrence of interaction behaviors. We call them guided nodes. However, the current application doesn’t take into account the guided nodes in KG. We explore the utility of guided nodes in KG. It is applied in repository recommendations. In this paper, we propose an end-to-end framework, namely Guided Node Graph Convolutional Network (GNGCN), which effectively captures the connections between entities by mining the influence of related nodes. We extract samples of each entity in KG as their guided nodes and then combine the information and bias of the guided nodes when computing the representation of a given entity. The guided nodes can be extended to multiple hops. We evaluate our model on a real-world Github dataset named Github-SKG and music recommendation dataset, and the experimental results show that the method outperforms the recommendation baselines and our model is much lighter than others.

In recent years, there has been a lot of research on deep learning for survival prediction in EHR (Electronic Health Record). At present, EHR usually contains multivariate time series data with missing values. How to better predict mortality based on such data is what many studies are currently doing. Most of the mortality prediction methods based on deep learning only pay attention to missing value filling or adjusting the model structure to enhance the mortality prediction performance, but they do not combine these two aspects well. In this paper, we propose MTSSP (Multivariate Time Series for Survival Prediction), a new method that combines missing value filling and time series classification. It takes two representations of the loss pattern: the mask and the time interval, which are combined into the recurrent neural network for the interpolation of the missing value of patient characteristics. When the missing data values are interpolated, the model combines bidirectional RNN and one-dimensional CNN to jointly capture the patient's medical behavior from a global and local perspective to enhance the representation ability of data information, thereby improving the prediction accuracy of the model. In the end, we conducted our mortality prediction experiments on the real-world emergency MIMIC-III dataset and MIMIC-IV dataset. The experimental results demonstrate that the proposed approach has been shown to significantly outperform other approaches.

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.

Abstract Microtask crowdsourcing is a form of crowdsourcing in which work is decomposed into a set of small, self-contained tasks, which each can typically be completed in a matter of minutes. Due to the various capabilities and knowledge background of the voluntary participants on the Internet, the answers collected from the crowd are ambiguous and the final answer aggregation is challenging. In this process, the choice of quality control strategies is important for ensuring the quality of the crowdsourcing results. Previous work on answer estimation mainly used expectation–maximization (EM) approach. Unfortunately, EM provides local optimal solutions and the estimated results will be affected by the initial value. In this paper, we extend the local optimal result of EM and propose an approximate global optimal algorithm for answer aggregation of crowdsourcing microtasks with binary answers. Our algorithm is expected to improve the accuracy of real answer estimation through further likelihood maximization. First, three worker quality evaluation models are presented based on static and dynamic methods, respectively, and the local optimal results are obtained based on the maximum likelihood estimation method. Then, a dominance ordering model (DOM) is proposed according to the known worker responses and worker categories for the specified crowdsourcing task to reduce the space of potential task-response sequence while retaining the dominant sequence. Subsequently, a Cut-point neighbor detection algorithm is designed to iteratively search for the approximate global optimal estimation in a reduced space, which works on the proposed dominance ordering model (DOM). We conduct extensive experiments on both simulated and real-world datasets, and the experimental results illustrate that the proposed approach can obtain better estimation results and has higher performance than regular EM-based algorithms.

Hospital readmission prediction employs reliable predictive models to evaluate the readmission risk of patients upon discharge. Identifying patients with high readmission risk and paying additional attention to them can ease the burden on both patients and society. Recently, considerable attention has been paid to personalized readmission predictions, i.e., building an independent model for each target patient. However, existing personalized predictive models can be easily affected by data insufficiency and provide poor generalization capabilities. To address these challenges, in this paper, we propose the CLusterbased mUlti-task lEarning model (CLUE) to achieve personalized hospital readmission prediction. CLUE groups patients into different clusters based on a multi-angle similarity metric to preserve the interrelated information of patients with highly similar clinical behaviors. Due to different group characteristics of patients, the clusters of patients are imbalanced. Given that, CLUE treats the hospital readmission prediction for each cluster of patients as one task and learns multiple tasks jointly by parameter sharing mechanisms. In this way, not only can the data insufficiency problem be alleviated by supplementing individual models with the shared information from other clusters, but also the specific information of each cluster can be preserved for personalization. We conduct extensive experiments on a real-world dataset of electronic health records, and show that CLUE significantly outperforms competitive comparative methods.

Abnormal co-occurrence medical visit behavior refers to the fraudulent behavior of fraudsters who hold multiple medical insurance cards and frequently purchase medicines within the same time period at the same location to defraud insurance medical insurance fund. Identifying abnormal co-occurrence medical visit behaviors plays a critical role in automated medical insurance fraud detection. However, the conventional methods mainly focus on the mining of frequent patterns, which may lead to misjudgement of normal patients who regularly seek medical treatment for a long time. In order to address this problem, we propose a novel biclustering algorithm, which finds suspicious patient groups who frequent purchased medicines within the same time period at the same location, and filters out normal patients from suspicious patients. Experimental results show that Biclutering-sim outperforms the competitor in detecting medical insurance fraud.

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.

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.

Early detection of diseases is a tricky problem for doctors due to the concealment of the disease itself and the patient's negligence. Electronic health records (EHRs) contain a wealth of patient information that can be used to assist in the early detection of diseases. The EHRs have the characteristics of high dimensionality and timing. The traditional non-sequential method using EHRs is not accurate for the prediction of disease and the calculation efficiency is relatively low. We use previous medical records of patients based on recurrent neural networks (RNNs) to present a prediction model named “RNN-INFO” for multiple diseases. The model can predict the probability and diagnosis of clinical visits simultaneously in a time window. The model reduces the dimension of the data through the hidden layer of the neural network and extracts the hidden representation between the medical records. In addition, in order to further improve the performance of the model, we introduce demographic information about patients. After data preprocessing, the final dataset of experiment we used contains more than 28 thousand patients and 289 thousand clinical visits. Our experiments are finished with the final dataset. The experimental results show that our model has better performance than non-temporal models on both probability prediction and diagnostic prediction. “RNN - INFO” gets better performance than RNN model without demographic information on probability prediction. It shows that the addition of demographic information can indeed be improved performance of probability prediction.

Cloud computing is a promising distributed computing platform for big data applications, e.g., scientific applications, since excessive resources can be obtained from cloud services for processing and storing both existing and generated application datasets. However, when tasks process big data stored in distributed data centers, the inevitable data movements will cause huge bandwidth cost and execution delay. In this paper, we construct a tripartite graph based model to formulate the data replica placement problem and propose a genetic algorithm based data replica placement strategy for scientific applications to reduce data transmissions in cloud. Our approach can reduce 1) the size of moved data, 2) the time of data movement and 3) the number of movements. We conduct experiments to compare the proposed strategy with the random placement strategy used in Hadoop Distributed Files System (HDFS), which demonstrates that our strategy has better performance for scientific applications in clouds.

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.

The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing fraud detection methods can be divided into two categories: pattern based and outlier detection based methods. However, because of the sparsity and complex granularity of big data, these methods have high false positive in fraud detection. In this paper, we propose an effective hybrid fraud detection method. We propose SSIsomap which improves isomap to cluster behaviors into behavior classes and propose SimLOF which improves LOF to conduct outlier detection, then we use Dempster-Shafer evidence Theory for combining behavior pattern evidence and outlier evidence, which yields a degree of belief of fraud to the new coming claim. The experiment result shows our method has significantly higher accuracy than exsiting methods in medical insurance fraud detection.

With the rapid development of the Internet, the Web is becoming the largest information repository of the world. Major efforts have been made in order to integrate the data of a specific domain on the Web. The traditional methods are largely done by few of system administrators which do not adapt to web scale. The construction of a web domain data integration system (WDDIS) becomes an urgent task The paper describes a new idea which asks the users to help the builders incrementally build WDDIS. It proposes an architecture of WDDIS and describes the mechanism of user collaboration. The approach shifts the enormous endeavors from the producers to the consumers which will promote WDDIS to be constructed quickly and effectively.

With the development of Internet, there are more and more Web service related to the travel. How to make these Web services work cooperatively so as to provide the travel schedule that satisfy the user 's requirement is the problem of the process study. In this paper, we propose a new process model facing individuals with travel as background. A new semantic description model is firstly put forward. Furthermore, we provide a method with which available services can be chosen and travel schedule automatically generated according to user 's requirements. The feasibility of this method is verified through its application to virtual travel agency.

When Web services are used to coalesce around the distributed applications, one prominent solution to manage and coordinate Web services is the use of process management. Many researches have been done to deal with automatic discovery and composition issues of Web services. However, the problem of how to run a process that is composed of distributed services is seldom considered. In this paper, based on our previous work, the smart process-based application model (SPM), we formalize smart process (SP) using algebra CCS, and discuss the benefits. In particular, we propose goal-based algorithms to run Smart Process.

With the growth of web service in quantity, it has become an urgent issue how to compose web service with effective method so as to capacitate web service providers to work cooperatively and exert greater function. The paper introduces web service composition method based on rules and meta-services. Meta services are the abstraction of web services with similar functions, while rules are used to describe logic relation and data dependence relation among meta-services. An algorithm that generates the abstract service composition process based on the rules and meta-services is designed. The abstract service composition process is converted into executable process by employing web service discovery. The method proposed in this paper is proved to be feasible by the application in travel platform.