Automatically detecting fake news has become increasingly necessary. Conventional approaches to fake news detection (FND) require a large number of training instances, which are not available in the scenario of new event FND (NEFND). More advanced methods address this problem through domain adaption (DA) to improve the overall performance of all events, or by transferring knowledge from source events. However, these methods either lack a target-oriented design or fail to perform effective transfer due to data scarcity in new events. This work focuses on the NEFND problem and proposes a multi-source selective transfer learning approach. Specifically, an integrated learner is built to make decisions, and an event-level transferability generator is designed to select more transfer-worthy source events, so as to achieve event-level selective transfer. Additionally, a two-stage training algorithm with a re-weighting optimization mechanism is also designed to highlight more transferable source instances, so as to achieve instance-level selective transfer and improve the performance on the target event. Experiments on the real-world multi-event fake news dataset that simulates the NEFND scenario are conducted to evaluate the effectiveness and superiority of the proposed approach.

Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.

Automatically detecting fake news as early as possible becomes increasingly necessary. Conventional approaches of fake news early detection (FNED) verify news' veracity with a predefined and indiscriminate detection position, which depends on domain experience and leads to unstable performance. More advanced methods address this problem with a proposed concept of adaptive detection position (ADP), i.e. the position where the veracity of the news record can be concluded. Yet these methods either lack theoretical reliability or weaken complex dependencies among multi-aspect clues, thus failing to provide practical and reasonable detection. This work focuses on the adaptive FNED problem and proposes a novel efficient and reliable deep state space model, namely AdaDebunk, which models the complex probabilistic dependencies. Specifically, a Bayes' theorem-based dynamic inference algorithm is designed to infer the ADPs and veracity, supporting the accumulation of multi-aspect clues. Besides, a training mechanism with hybrid loss is also designed to solve the over-/under-fitting problems, which further trades off the performance and generalization ability. Experiments on two real-world fake news datasets are conducted to evaluate the effectiveness and superiority of AdaDebunk. Compared with the state-of-the-art baselines, AdaDebunk achieves a 10% increase in F1 performance. Meanwhile, a case study is provided to demonstrate the reliability of AdaDebunk as well as our research motivation.

Ubiquitous Internet of Things (IoT) sensors in the smart city generate various urban utility sequential data, such as electricity and water usage records, which are defined as multivariate utility time series (MUTS). Due to the complex behavior of human beings, MUTS contains more complicated relationships, which go beyond general multivariate time series (TS). Specifically, multifaceted temporal couplings exist in MUTS, including intra-/inter-TS, short-to-long term, evolving, and polarized (positive/negative) relationships. Existing multisequence predictors including the latest deep-learning methods either weaken short-to-long term representation or omit evolving and polarized couplings. This work focuses on MUTS sensory data representation and prediction and proposes a novel approach—CoupledMUTS for multifaceted temporal coupling relational learning. MUTS representation module generates multidimensional representations to reveal short-to-long temporal couplings in MUTS. Gated coupling units (GCUs) module learns evolving couplings by filtering weak positive/negative relations. And dual-stage fusion module integrates multifaceted temporal couplings in both intra-TS and inter-TS for prediction. Extensive experiments on two real-world utility data sets demonstrate that our method outperforms existing shallow and deep models in utility demand prediction.

Recently,fake newshas been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namelyprogressive detection, which can be achieved by thedynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to theLabeled Variable Dimension Kalman Filter(LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.

The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.