Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201

TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.Comment: 14 page

Research on Real Time Traffic Information Data Model and Its Data Transmit

Real-time of geographic information system for transportation (GIS-T) is one of the essential conditions to alleviate the traffic jam and guide the traffic flow rationally. In order to make it convenient for sharing and maintaining data, this paper structures the independent real-time traffic information database, seamless merging real-time traffic information and GIS data through data fusion method. In order to realize this purpose, the paper research on baseline network data model, baseline network is composed of base points and baselines. Base points are exclusive locating on the road network, which can be determined in field, and also can be resumed. Baseline is line element, which replaces traffic event, the baseline locate road network by the point, and therefore, it is easy to realize data share for various linear reference system. According to the data model, designing structure and introducing data transmit flow of the Geographic Information System for Transportation. Key words: Data Model; Data Fusion; GIS; Traffic Information This paper is supported by the Department of Instrument Science and Engineering, Southeast University, and professor DE-JUN WAN and professor QING WAN