Neural Programming by Example

Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to-end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs.Comment: 7 pages, Association for the Advancement of Artificial Intelligence (AAAI

MixUp as Locally Linear Out-Of-Manifold Regularization

MixUp is a recently proposed data-augmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their labels. Training deep neural networks with such additional data is shown capable of significantly improving the predictive accuracy of the current art. The power of MixUp, however, is primarily established empirically and its working and effectiveness have not been explained in any depth. In this paper, we develop an understanding for MixUp as a form of "out-of-manifold regularization", which imposes certain "local linearity" constraints on the model's input space beyond the data manifold. This analysis enables us to identify a limitation of MixUp, which we call "manifold intrusion". In a nutshell, manifold intrusion in MixUp is a form of under-fitting resulting from conflicts between the synthetic labels of the mixed-up examples and the labels of original training data. Such a phenomenon usually happens when the parameters controlling the generation of mixing policies are not sufficiently fine-tuned on the training data. To address this issue, we propose a novel adaptive version of MixUp, where the mixing policies are automatically learned from the data using an additional network and objective function designed to avoid manifold intrusion. The proposed regularizer, AdaMixUp, is empirically evaluated on several benchmark datasets. Extensive experiments demonstrate that AdaMixUp improves upon MixUp when applied to the current art of deep classification models.Comment: Accepted by AAAI201

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Absolute height measurement of specular surfaces with modified active fringe reflection photogrammetry

Deflectometric methods have existed for more than a decade for slope measurement of specular freeform surfaces through utilization of the deformation of a sample pattern after reflection from a test surface. Usually, these approaches require two-directional fringe patterns to be projected on a LCD screen or ground glass and require slope integration, which leads to some complexity for the whole measuring process. This paper proposes a new mathematical measurement model for measuring topography information of freeform specular surfaces, which integrates a virtual reference specular surface into the method of active fringe reflection delfectometry and presents a straight-forward relation between height and phase. This method only requires one direction of horizontal or vertical sinusoidal fringe patterns to be projected on a LCD screen, resulting in a significant reduction in capture time over established method. Assuming the whole system has been pre-calibrated, during the measurement process, the fringe patterns are captured separately via the virtual reference and detected freeform surfaces by a CCD camera. The reference phase can be solved according to spatial geometrical relation between LCD screen and CCD camera. The captured phases can be unwrapped with a heterodyne technique and optimum frequency selection method. Based on this calculated unwrapped-phase and that proposed mathematical model, absolute height of the inspected surface can be computed. Simulated and experimental results show that this methodology can conveniently calculate topography information for freeform and structured specular surfaces without integration and reconstruction processes

Relay Selection for Bidirectional AF Relay Network with Outdated CSI

Most previous researches on bidirectional relay selection (RS) typically assume perfect channel state information (CSI). However, outdated CSI, caused by the the time-variation of channel, cannot be ignored in the practical system, and it will deteriorate the performance. In this paper, the effect of outdated CSI on the performance of bidirectional amplify-and-forward RS is investigated. The optimal single RS scheme in minimizing the symbol error rate (SER) is revised by incorporating the outdated channels. The analytical expressions of end-to-end signal to noise ratio (SNR) and symbol error rate (SER) are derived in a closed-form, along with the asymptotic SER expression in high SNR. All the analytical expressions are verified by the Monte-Carlo simulations. The analytical and the simulation results reveal that once CSI is outdated, the diversity order degrades to one from full diversity. Furthermore, a multiple RS scheme is proposed and verified that this scheme is a feasible solution to compensate the diversity loss caused by outdated CSI.Comment: accepted by IEEE Transactions on Vehicular Technolog