Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, same object, there usually exist conflicts among multi-source information. To tackle this challenge, truth discovery, which integrates noisy by estimating reliability each source, has emerged as a hot topic. Several discovery methods have been proposed various scenarios, and they successfully applied in diverse application domains. In survey, we focus on providing...

In the era of Big Data, data entries, even describing same objects or events, can come from a variety sources, where source be web page, database person. Consequently, conflicts among sources become inevitable. To resolve and achieve high quality data, truth discovery crowdsourcing aggregation have been studied intensively. However, although these two topics lot in common, they are separately applied to different domains. answer need systematic introduction comparison topics, we present an...

Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, quality such human-generated sensory data varies significantly among different users. To better utilize data, problem truth discovery, whose goal is estimate user and infer reliable aggregated results through quality-aware aggregation, has emerged as a hot topic. Although existing discovery approaches can provide results, they fail protect private information individual Moreover,...

Previous chapter Next Full AccessProceedings Proceedings of the 2013 SIAM International Conference on Data Mining (SDM)On Handling Negative Transfer and Imbalanced Distributions in Multiple Source LearningLiang Ge, Jing Gao, Hung Ngo, Kang Li, Aidong ZhangLiang Zhangpp.261 - 269Chapter DOI:https://doi.org/10.1137/1.9781611972832.29PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract learning has benefited many real-world applications where labeled...

Return Oriented Programming (ROP) chains attack has been widely used to bypass Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) protection. However, the generation technology for ROP is still in a state of manual coding. While, current techniques automatically generating are insufficiently researched have few successful applications. On other hand, existing methods based on using Intermediate Language (IL) which order translate semantics original instructions...

considering the problem of uncertainty data in information gathering system, a multi-sensor fusion method based on fuzzy set and evidence theory (FS-DS) is proposed . The support probability uncertain defined by making using correlation function , Then it received credibility measured each sensor form membership function. And will confidence into basic Finally, sensors with higher measurement precision are identified D-S combination. can improve assignment difficult to be determined...

Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, data are privately distributed across multiple owners. Thus, this dilemma raises the interest in how to perform LLM federated learning (FL). confronted limited computation and communication capacities, FL clients struggle fine-tune an effectively. To end, we introduce FedBiOT, a resource-efficient approach FL. Specifically, our method involves server...

Sequential data modeling has received growing interests due to its impact on real world problems. is ubiquitous - financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in how capture the strong hidden correlations among complex features high volumes. The sparsity skewness extracted from also add complexity problem. In this paper, we address these challenges both discriminative generative perspectives, propose novel...

In this work, we propose a self-supervised learning model based on the transformer framework, using it to impute missing data in multivariate time series. Unlike selfsupervised models NLP and CV fields, always trains huge pre-trained model, which can fine-tune well other datasets same field. Our Multivariate Time Series Based Maksed AutoEncoding (MTAE) uses position encoding mask matrix that are more suitable for series while keeping general framework of unchanged, take standard information...

With the explosive growth of data, how to efficiently cluster large-scale unlabeled data has become an important issue that needs be solved urgently. Especially in face real-world which contains a large number complex distributions noises and outliers, research on robust clustering algorithms one hottest topics. In response this issue, algorithm based correntropy (RLSCC) is proposed paper, specifically, k-means firstly applied generated pseudo-labels reduce input scale subsequent spectral...

Discipline inspection and supervision laws regulations are an important basis for discipline personnel. In this study, the data of in field were firstly obtained from latest "Regulations on Punishment Communist Party China" (fourth edition), knowledge graph was constructed after pretreatment. Secondly, based regulations, core semantic matching algorithm question answering system is tested TF-IDF model with highest accuracy TOP1 selected to finally realize regulations.

We present the fundamental challenges for dynamic provisioning of big data applications. The findings are based on our previous experience in this domain, as well a comprehensive study selected set state-of-the-art tools ecosystem. then incorporate these framework aiming at dynamically applications services containerised cloud. innovations behind to optimise whole lifecycle holistic manner by adoption microservices(μServices) methodologies. feasibility approach is verified through case...