Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user or item into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. The user module learns to attend to only those signals that are relevant with respect to the target item, whereas the item module learns to extract the most salient contents with regard to properties of the item. Our multi-hierarchical paradigm accounts for the fact that neither are all reviews equally useful, nor are all sentences within each review equally pertinent. Extensive experimental results on a variety of real datasets demonstrate the effectiveness of our method

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.Comment: This paper is accepted by AAAI 202

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively

Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring

Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change