Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant privacy risks, especially from data reconstruction attacks that recover original inputs from intermediate representations. Existing defenses using noise injection often degrade model performance. To overcome these challenges, we present PM-SFL, a scalable and privacy-preserving SFL framework that incorporates Probabilistic Mask training to add structured randomness without relying on explicit noise. This mitigates data reconstruction risks while maintaining model utility. To address data heterogeneity, PM-SFL employs personalized mask learning that tailors submodel structures to each client's local data. For system heterogeneity, we introduce a layer-wise knowledge compensation mechanism, enabling clients with varying resources to participate effectively under adaptive model splitting. Theoretical analysis confirms its privacy protection, and experiments on image and wireless sensing tasks demonstrate that PM-SFL consistently improves accuracy, communication efficiency, and robustness to privacy attacks, with particularly strong performance under data and system heterogeneity.

Ride-hailing platforms (e.g., Uber and Didi Chuxing) have become increasingly popular in recent years.Efficiencyhas always been an important metric for such platforms. However, only focusing on efficiency inevitably ignores thefairnessof driver incomes, which could impair the sustainability of ride-hailing systems. To optimize such two essential objectives,order dispatchinganddriver repositioningplay an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. In practice, the platform offers monetary incentives to drivers for completing the repositioning and has a budget for the repositioning cost. Therefore, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both long-term efficiency and fairness in ride-hailing under the budget constraint. To this end, we propose JDRCL, a novel multi-agent reinforcement learning framework, which integrates a group-based action representation that copes with the variable action space, and a primal-dual iterative training algorithm to learn a constraint-satisfying policy that maximizes both the worst and the overall incomes of drivers. Furthermore, we prove the asymptotic convergence rate of our training algorithm. Extensive experiments based on three real-world ride-hailing order datasets show that JDRCL outperforms state-of-the-art baselines on both efficiency and fairness.

The ride-hailing service offered by mobility-on-demand platforms, such as Uber and Didi Chuxing, has greatly facilitated people's traveling and commuting, and become increasingly popular in recent years. Efficiency (e.g., gross merchandise volume) has always been an important metric for such platforms. However, only focusing on the efficiency inevitably ignores the fairness of driver incomes, which could impair the sustainability of the overall ride-hailing system in the long run. To optimize the aforementioned two essential metrics, order dispatching and driver repositioning play an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. Thus, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness for ride-hailing platforms. To address this problem, we propose a novel multi-agent reinforcement learning framework, referred to as JDRL, to help drivers make distributed order selection and repositioning decisions. Specifically, to cope with the variable action space, JDRL segments the action space into a fixed number of action groups, and fixes the policy output dimension for order selection as the number of action groups. In terms of the fairness criterion, JDRL adopts the max-min fairness, and augments the vanilla policy gradient to an iterative training algorithm that alternates between a minimization step and a policy improvement step to maximize both the worst and the overall performance of agents. In addition, we provide the theoretical convergence guarantee of our JDRL training algorithm even under non-convex policy networks and stochastic gradient updating. Extensive experiments are conducted with three public real-world ride-hailing order datasets, including over 2 million orders in Haikou, China, over 5 million orders in Chengdu, China, and over 6 million orders in New York City, USA. Experimental results show that JDRL demonstrates a consistent advantage compared to state-of-the-art baselines in terms of both efficiency and fairness. To the best of our knowledge, this is the first work that exploits joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness in a ride-hailing system.

The past decades have witnessed significant progress towards improving the accuracy of predictions powered by complex machine learning models. Despite much success, the lack of model interpretability prevents the usage of these techniques in life-critical systems such as medical diagnosis and self-driving systems. Recently, the interpretability issue has received much attention, and one critical task is to explain why a predictive model makes a specific decision. We refer to this task as outcome interpretation. Many outcome interpretation methods have been developed to produce human-understandable interpretations by utilizing intermediate results of the machine learning models, such as gradients and model parameters.

Recent studies reveal that recommender systems are vulnerable to data poisoning attack due to their openness nature. In data poisoning attack, the attacker typically recruits a group of controlled users to inject well-crafted user-item interaction data into the recommendation model's training set to modify the model parameters as desired. Thus, existing attack approaches usually require full access to the training data to infer items' characteristics and craft the fake interactions for controlled users. However, such attack approaches may not be feasible in practice due to the attacker's limited data collection capability and the restricted access to the training data, which sometimes are even perturbed by the privacy preserving mechanism of the service providers. Such design-reality gap may cause failure of attacks. In this paper, we fill the gap by proposing two novel adversarial attack approaches to handle the incompleteness and perturbations in user-item interaction data. First, we propose a bi-level optimization framework that incorporates a probabilistic generative model to find the users and items whose interaction data is sufficient and has not been significantly perturbed, and leverage these users and items' data to craft fake user-item interactions. Moreover, we reverse the learning process of recommendation models and develop a simple yet effective approach that can incorporate context-specific heuristic rules to handle data incompleteness and perturbations. Extensive experiments on two datasets against three representative recommendation models show that the proposed approaches can achieve better attack performance than existing approaches.

Integration of information on road transverse geometric features such as cross slope and superelevation in digital maps can widen the scope of its applications, which is primarily navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS). The huge scale and dynamic nature of road networks make sensing such road geometric features a challenging task. Traditional methods oftentimes suffer from high cost, limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road transverse slope estimation framework using sensor data from smartphones. Based on error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and GPS to estimate road transverse slope profile of a road segment. To improve accuracy and robustness of the system, the estimations of road transverse slope from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of 9km demonstrates the superior performance of our proposed method, achieving 350% improvement on road transverse slope estimation accuracy over existing methods, with 90% of errors below 0.5°.

The goal of metric learning is to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. An implicit assumption in the traditional settings of metric learning is that the associated labels of the instances are deterministic. However, in many real-world applications, the associated labels come naturally with probabilities instead of deterministic values, which makes it difficult for the existing metric-learning methods to work well in these applications. To address this challenge, in this article, we study how to effectively learn the distance metric from datasets that contain probabilistic information, and then propose several novel metric-learning mechanisms for two types of probabilistic labels, i.e., the instance-wise probabilistic label and the group-wise probabilistic label. Compared with the existing metric-learning methods, our proposed mechanisms are capable of learning distance metrics directly from the probabilistic labels with high accuracy. We also theoretically analyze the proposed mechanisms and conduct extensive experiments on real-world datasets to verify the desirable properties of these mechanisms.

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better footprints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments on a wide range of sensing inputs, including motion sensors, WiFi, ultrasound, and visible light. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs 1.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

Detecting local events (e.g., protests, accidents) in real-time is an important task needed by a wide spectrum of real-world applications. In recent years, with the proliferation of social media platforms, we can access massive geo- tagged social messages, which can serve as a precious resource for timely local event detection. However, existing local event detection methods either suffer from unsatisfactory performances or need intensive annotations. These limitations make existing methods impractical for large-scale applications. Through the analysis of real-world datasets, we found that the informativeness level of social media users, which is neglected by existing work, plays a highly critical role in distilling event-related information from noisy social media contexts. Motivated by this finding, we propose an unsupervised framework, named LEDetect, to estimate the informativeness level of social media users and leverage the power of highly informative users for local event detection. Experiments on a large-scale real-world dataset show that the proposed LEDetect model can improve the performance of event detection compared with the state-of-the-art unsupervised approach. Also, we use case studies to show that the events discovered by the proposed model are of high quality and the extracted highly informative users are reasonable.

Recent advances in attention networks have gained enormous interest in time series data mining. Various attention mechanisms are proposed to soft-select relevant timestamps from temporal data by assigning learnable attention scores. However, many real-world tasks involve complex multivariate time series that continuously measure target from multiple views. Different views may provide information of different levels of quality varied over time, and thus should be assigned with different attention scores as well. Unfortunately, the existing attention-based architectures cannot be directly used to jointly learn the attention scores in both time and view domains, due to the data structure complexity. Towards this end, we propose a novel multi-view attention network, namely MuVAN, to learn fine-grained attentional representations from multivariate temporal data. MuVAN is a unified deep learning model that can jointly calculate the two-dimensional attention scores to estimate the quality of information contributed by each view within different timestamps. By constructing a hybrid focus procedure, we are able to bring more diversity to attention, in order to fully utilize the multi-view information. To evaluate the performance of our model, we carry out experiments on three real-world benchmark datasets. Experimental results show that the proposed MuVAN model outperforms the state-of-the-art deep representation approaches in different real-world tasks. Analytical results through a case study demonstrate that MuVAN can discover discriminative and meaningful attention scores across views over time, which improves the feature representation of multivariate temporal data.

Metric learning aims to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. In the traditional settings of metric learning, an implicit assumption is that the associated labels of the instances are deterministic. However, in many real-world applications, the associated labels come naturally with probabilities instead of deterministic values. Thus, the existing metric learning methods cannot work well in these applications. To tackle this challenge, in this paper, we study how to effectively learn the distance metric from datasets that contain probabilistic information, and then propose two novel metric learning mechanisms for two types of probabilistic labels, i.e., the instance-wise probabilistic label and the group-wise probabilistic label. Compared with the existing metric learning methods, our proposed mechanisms are capable of learning distance metrics directly from the probabilistic labels with high accuracy. We also theoretically analyze the two proposed mechanisms and provide theoretical bounds on the sample complexity for both of them. Additionally, extensive experiments based on real-world datasets are conducted to verify the desirable properties of the proposed mechanisms.

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.

As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.

Truth discovery has attracted increasingly more attention due to its ability to distill trustworthy information from noisy multi-sourced data without any supervision. However, most existing truth discovery methods are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data as text data has its unique characteristics. The major challenges of inferring true information on text data stem from the multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). To tackle these challenges, in this paper, we propose a novel truth discovery method, named "TextTruth", which jointly groups the keywords extracted from the answers of a specific question into multiple interpretable factors, and infers the trustworthiness of both answer factors and answer providers. After that, the answers to each question can be ranked based on the estimated trustworthiness of factors. The proposed method works in an unsupervised manner, and thus can be applied to various application scenarios that involve text data. Experiments on three real-world datasets show that the proposed TextTruth model can accurately select trustworthy answers, even when these answers are formed by multiple factors.

Truth discovery, with the goal of inferring true information from massive data through aggregating the information from multiple data sources, has attracted significant attention in recent years. It has demonstrated great advantages in real applications since it can automatically learn the reliability degrees of the data sources without supervision and in turn helps to find more reliable information. In many applications, however, the data may arrive in a stream and present various temporal patterns. Unfortunately, there is no existing truth discovery work that can handle such time series data. To tackle this challenge, we propose a novel online truth discovery framework that incorporates the predictions on the time series data into the truth estimation process. By jointly considering the multi-source information and the temporal patterns of the time series data, the proposed framework can improve the accuracy of the truth discovery results as well as the time series prediction. The effectiveness of the proposed framework is validated on both synthetic and real-world datasets.

As an effective way to solicit useful information from the crowd, crowdsourcing has emerged as a popular paradigm to solve challenging tasks. However, the data provided by the participating workers are not always trustworthy. In real world, there may exist malicious workers in crowdsourcing systems who conduct the data poisoning attacks for the purpose of sabotage or financial rewards. Although data aggregation methods such as majority voting are conducted on workers» labels in order to improve data quality, they are vulnerable to such attacks as they treat all the workers equally. In order to capture the variety in the reliability of workers, the Dawid-Skene model, a sophisticated data aggregation method, has been widely adopted in practice. By conducting maximum likelihood estimation (MLE) using the expectation maximization (EM) algorithm, the Dawid-Skene model can jointly estimate each worker»s reliability and conduct weighted aggregation, and thus can tolerate the data poisoning attacks to some degree. However, the Dawid-Skene model still has weakness. In this paper, we study the data poisoning attacks against such crowdsourcing systems with the Dawid-Skene model empowered. We design an intelligent attack mechanism, based on which the attacker can not only achieve maximum attack utility but also disguise the attacking behaviors. Extensive experiments based on real-world crowdsourcing datasets are conducted to verify the desirable properties of the proposed mechanism.

In our daily lives, travel takes up an important part, and many trips are generated everyday, such as going to school or shopping. With the widely adoption of GPS-integrated devices, a large amount of trips can be recorded with GPS trajectories. These trajectories are represented by sequences of geo-coordinates and can help us answer simple questions such as “where did you go”. However, there is another important question awaiting to be answered, that is “what did/will you do”, i.e., the trip purpose inference. In practice, people's trip purposes are very important in understanding travel behaviors and estimating travel demands. Obviously, it is very challenging to infer trip purposes solely based on the trajectories, because the GPS devices are not accurate enough to pinpoint the venues visited. In this paper, we infer individual's trip purposes by combining the knowledge from heterogeneous data sources including trajectories, POIs and social media data. The proposed dynamic Bayesian network model captures three important factors: the sequential properties of trip activities, the functionality and POI popularity of trip end areas. Extensive experiments are conducted on real-world data sets with trajectories of 8,361 residents and the 6.9 million geo-tagged tweets in the Bay area. Experimental results demonstrate the advantages of the proposed method on correctly inferring the trip purposes.

The traffic volume on road segments is a vital property of the transportation efficiency. City-wide traffic volume information can benefit people with their everyday life, and help the government on better city planning. However, there are no existing methods that can monitor the traffic volume of every road, because they are either too expensive or inaccurate. Fortunately, nowadays we can collect a large amount of urban data which provides us the opportunity to tackle this problem. In this paper, we propose a novel framework to infer the city-wide traffic volume information with data collected by loop detectors and taxi trajectories. Although these two data sets are incomplete, sparse and from quite different domains, the proposed spatio-temporal semi-supervised learning model can take the full advantages of both data and accurately infer the volume of each road. In order to provide a better interpretation on the inference results, we also derive the confidence of the inference based on spatio-temporal properties of traffic volume. Real-world data was collected from 155 loop detectors and 6,918 taxis over a period of 17 days in Guiyang China. The experiments performed on this large urban data set demonstrate the advantages of the proposed framework on correctly inferring the traffic volume in a city-wide scale.

In the big data era, the information about the same object collected from multiple sources is inevitably conflicting. The task of identifying true information (i.e., the truths) among conflicting data is referred to as truth discovery, which incorporates the estimation of source reliability degrees into the aggregation of multi-source data. However, in many real-world applications, large-scale data are distributed across multiple servers. Traditional truth discovery approaches cannot handle this scenario due to the constraints of communication overhead and privacy concern. Another limitation of most existing work is that they ignore the differences among objects, i.e., they treat all the objects equally. This limitation would be exacerbated in distributed environments where significant differences exist among the objects. To tackle the aforementioned issues, in this paper, we propose a novel distributed truth discovery framework (DTD), which can effectively and efficiently aggregate conflicting data stored across distributed servers, with the differences among the objects as well as the importance level of each server being considered. The proposed framework consists of two steps: the local truth computation step conducted by each local server and the central truth estimation step taking place in the central server. Specifically, we introduce the uncertainty values to model the differences among objects, and propose a new uncertainty-based truth discovery method (UbTD) for calculating the true information of objects in each local server. The outputs of the local truth computation step include the estimated local truths and the variances of objects, which are the input information of the central truth estimation step. To infer the final true information in the central server, we propose a new algorithm to aggregate the outputs of all the local servers with the quality of different local servers taken into account. The proposed distributed truth discovery framework can infer object truths without delivering any raw data to the central server, and thus can reduce communication overhead as well as preserve data privacy. Experimental results on three real world datasets show that the proposed DTD framework can efficiently estimate object truths with accuracy guarantee, and the proposed UbTD algorithm significantly outperforms the state-of-the-art batch truth discovery approaches.

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.

In the age of big data, information for the same entity can be obtained from different sources, which is inevitably conflicting. Therefore, aggregation methods are needed to identify the trustworthy information from such conflicting data. Truth discovery, which improves the aggregation results by estimating source trustworthiness and discovering truths simultaneously, has become an emerging field. Most truth discovery methods assume that sources make their claims independently, which may not be true in practice. As a matter of fact, influences among sources are ubiquitous and the claims made by one source may be influenced by others. Although there is some work that considers source correlation, those methods are designed to handle categorical claims, which is not general enough to represent the complicated real world applications. To tackle these challenges in truth discovery, we propose an unsupervised probabilistic model named IATD. The model takes source correlations as prior for influence derivation. To model influences among sources, we introduce "claim trustworthiness", which fuses the trustworthiness of the source which provides the claim and the trustworthiness of its influencers. Besides, the proposed model can handle different data types using different distributions in the probabilistic model. Experiments on real-world datasets show that IATD model can improve the aggregation performance compared with the state-of-the-art truth discovery approaches. The properties of IATD model are further illustrated using simulated datasets.

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.

In the information age, people can easily collect information about the same set of entities from multiple sources, among which conflicts are inevitable. This leads to an important task, truth discovery, i.e., to identify true facts (truths) via iteratively updating truths and source reliability. However, the convergence to the truths is never discussed in existing work, and thus there is no theoretical guarantee in the results of these truth discovery approaches. In contrast, in this paper we propose a truth discovery approach with theoretical guarantee. We propose a randomized gaussian mixture model (RGMM) to represent multi-source data, where truths are model parameters. We incorporate source bias which captures its reliability degree into RGMM formulation. The truth discovery task is then modeled as seeking the maximum likelihood estimate (MLE) of the truths. Based on expectation-maximization (EM) techniques, we propose population-based (i.e., on the limit of infinite data) and sample-based (i.e., on a finite set of samples) solutions for the MLE. Theoretically, we prove that both solutions are contractive to an ε-ball around the MLE, under certain conditions. Experimentally, we evaluate our method on both simulated and real-world datasets. Experimental results show that our method achieves high accuracy in identifying truths with convergence guarantee.

In the past decade, commercial crowdsourcing platforms have revolutionized the ways of classifying and annotating data, especially for large datasets. Obtaining labels for a single instance can be inexpensive, but for large datasets, it is important to allocate budgets wisely. With limited budgets, requesters must trade-off between the quantity of labeled instances and the quality of the final results. Existing budget allocation methods can achieve good quantity but cannot guarantee high quality of individual instances under a tight budget. However, in some scenarios, requesters may be willing to label fewer instances but of higher quality. Moreover, they may have different requirements on quality for different tasks. To address these challenges, we propose a flexible budget allocation framework called Requallo. Requallo allows requesters to set their specific requirements on the labeling quality and maximizes the number of labeled instances that achieve the quality requirement under a tight budget. The budget allocation problem is modeled as a Markov decision process and a sequential labeling policy is produced. The proposed policy greedily searches for the instance to query next as the one that can provide the maximum reward for the goal. The Requallo framework is further extended to consider worker reliability so that the budget can be better allocated. Experiments on two real-world crowdsourcing tasks as well as a simulated task demonstrate that when the budget is tight, the proposed Requallo framework outperforms existing state-of-the-art budget allocation methods from both quantity and quality aspects.

In many applications, one can obtain descriptions about the same objects or events from a variety of sources. As a result, this will inevitably lead to data or information conflicts. One important problem is to identify the true information (i.e., thetruths) among conflicting sources of data. It is intuitive to trust reliable sources more when deriving the truths, but it is usually unknown which one is more reliablea priori. Moreover, each source possesses a variety of properties with different data types. An accurate estimation of source reliability has to be made by modeling multiple properties in a unified model. Existing conflict resolution work either does not conduct source reliability estimation, or models multiple properties separately. In this paper, we propose to resolve conflicts among multiple sources of heterogeneous data types. We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the truths and the multi-source observations where each source is weighted by its reliability. Different loss functions can be incorporated into this framework to recognize the characteristics of various data types, and efficient computation approaches are developed. The proposed framework is further adapted to deal with streaming data in an incremental fashion and large-scale data in MapReduce model. Experiments on real-world weather, stock, and flight data as well as simulated multi-source data demonstrate the advantage of jointly modeling different data types in the proposed framework.

In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.

In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users/sources. FaitCrowd jointly models the process of generating question content and sources' provided answers in a probabilistic model to estimate both topical expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, FaitCrowd demonstrates better ability to obtain true answers for the questions compared with existing approaches. Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.

In many real world applications, the same item may be described by multiple sources. As a consequence, conflicts among these sources are inevitable, which leads to an important task: how to identify which piece of information is trustworthy, i.e., the truth discovery task. Intuitively, if the piece of information is from a reliable source, then it is more trustworthy, and the source that provides trustworthy information is more reliable. Based on this principle, truth discovery approaches have been proposed to infer source reliability degrees and the most trustworthy information (i.e., the truth) simultaneously. However, existing approaches overlook the ubiquitous long-tail phenomenon in the tasks, i.e., most sources only provide a few claims and only a few sources make plenty of claims, which causes the source reliability estimation for small sources to be unreasonable. To tackle this challenge, we propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. The proposed method not only estimates source reliability, but also considers the confidence interval of the estimation, so that it can effectively reflect real source reliability for sources with various levels of participation. Experiments on four real world tasks as well as simulated multi-source long-tail datasets demonstrate that the proposed method outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.

In typical studies of node grouping detection, the grouping is presumed to have a certain type of correlation with the network structure (e.g., densely connected groups of nodes that are loosely connected in between). People have defined different fitness measures (modularity, conductance, etc.) to quantify such correlation, and group the nodes by optimizing a certain fitness measure. However, a particular grouping with desired semantics, as the target of the detection, is not promised to be detectable by each measure. We study a fundamental problem in the process of node grouping discovery: Given a particular grouping in a network, whether and to what extent it can be discovered with a given fitness measure. We propose two approaches of testing the detectability, namely ranking-based and correlation-based randomization tests. Our methods are evaluated on both synthetic and real datasets, which shows the proposed methods can effectively predict the detectability of groupings of various types, and support explorative process of node grouping discovery.