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Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional...
Collaborative filtering is widely used in recommender systems. When training data are extremely sparse, neighbor selection methods work ineffectively. To address this issue, this paper proposes a distributed representation model that represents users as low-dimensional vectors for neighbor selection by considering the chronological order of users' ratings. Experiments show that the proposed method...
Context-Aware Recommendation Systems has gained lots of attention in both industry and academic research. Factorization Machines (FM) based recommendation has been successfully used in sparse industrial datasets for user personalized video recommendations. FM is a collaborative filtering technique for predicting a target such as user rating, given observations of interaction between some users and...
In this paper we overview the modern forecast methods of monthly sunspot numbers, such as McNish-Lincoln and Hathaway-Wilson-Reichmann standard curve-fitting. Their disadvantages are presented, leading us to the necessity of researching a new technique for the solar activity prediction. For the long-term forecast we propose to use the established nonlinear dynamo model based on negative effective...
Supply chain management aims at delivering goods in the shortest time at the lowest possible price while ensuring the best possible quality and is now vital to the success of the online retail business. Executing effective warehouse site selection has been one of the key challenges in the development of a successful supply chain system. While some effective strategies for warehouse site selection...
Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive...
In biomedical research, events revealing complex relations between entities play an important role. Event trigger identification is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in the previous work: (1) Traditional feature-based methods often rely on human ingenuity, which is a time-consuming process. Though most representation-based...
Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative...
Given an undirected network where some of the nodes are labeled, how can we classify the unlabeled nodes with high accuracy? Loopy Belief Propagation (LBP) is an inference algorithm widely used for this purpose with various applications including fraud detection, malware detection, web classification, and recommendation. However, previous methods based on LBP have problems in modeling complex structures...
The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian...
This paper describes a latent space understandable network: Self- excited Generative Adversarial Network (Self- ExGAN), a novel self- excited structure based on adversarial learning. Compared with the conventional generative adversarial networks, SelfExGAN consists of three components, which are en- coder (E), generator (G), and discriminator (D). Different from other papers which directly apply reconstruction...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So...
Post-database searching is a key procedure for peptide spectrum matches (PSMs) in protein identification with mass spectrometry-based strategies. Although many machine learning-based approaches have been developed to improve the accuracy of peptide identification, the challenge remains for improvement due to the poor quality of data samples. CRanker has shown its effectiveness and efficiency in terms...
The multilayer perceptrons (MLPs) have been widely used in many communication applications, however, the learning process of the multilayer perceptrons often becomes very slow, which is due to the existence of the singularities in the parameter space. As the singularities significantly affect the learning dynamics of MLPs, the standard gradient descent method is not Fisher efficient. In order to overcome...
A new methodology for image synthesis based on two cooperative training ConvNets is proposed. Two generative ConvNets and unsupervised joint learning are designed to effectively reflect the characteristics of real scenery and image pattern. Every ConvNet is directly derived from the discriminate ConvNet and has the potential to learn from big unlabeled data, either by contrastive divergence. One ConvNet...
In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting...
We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the...
A large project (e.g., Ubuntu) usually contains a large number of software packages. Sometimes the same bug report in such project would affect multiple packages, and developers of different packages need to collaborate with one another to fix the bug. Unfortunately, the total number of packages involved in a project like Ubuntu is relatively large, which makes it time-consuming to manually identify...
The questions of training simulators development technique for oil and gas industry automation control systems are considered. The possibility of using this technique for control systems based on mathematical and algorithmic control models is introduced. A mathematical model is used. The training simulator operating result is the process studying, learning management skills, adjusting parameters to...
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