Yong Shi

Yong Shi
University of Nebraska at Omaha | UN Omaha · College of Information Science and Technology

About

486
Publications
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9,875
Citations

Publications

Publications (486)
Article
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In China, environmental pollution responsibilities are divided according to administrative regions. However, because of the strong externality of environmental pollution, the movement of environmental pollution undoubtedly increases the complexity of pollution governance. To divide the responsibility of environmental pollution governance in each pr...
Article
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In recent years, deep neural networks (DNNs) have attracted extensive attention due to their excellent performance in many fields of vision and speech recognition. With the increasing scale of tasks to be solved, the network used is becoming wider and deeper, which requires millions or even billions of parameters. The deep and wide network with man...
Article
In this paper, we tackle a new learning paradigm called learning from complementary labels, where the training data specifies classes that instances do not belong to, instead of the accuracy labels. In general, it is more efficient to collect the complementary labels compared with collecting the supervised ones, with no need for selecting the corre...
Article
In this paper, we tackle the problem called learning from label proportions (LLP), where the training data is arranged into various bags, with only the proportions of different categories in each bag available. Existing efforts mainly focus on training a model with only the limited proportion information in a weakly supervised manner, thus result i...
Article
Due to the extraordinary abilities in extracting complex patterns, graph neural networks (GNNs) have demonstrated strong performances and received increasing attention in recent years. Despite their prominent achievements, recent GNNs do not pay enough attention to discriminate nodes when determining the information sources. Some of them select inf...
Article
Full-text available
Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management...
Article
This study evaluates the dynamic impact of various policies adopted by U.S. states, including social distancing, financial assistance, and vaccination policies. We propose a time-varying parameter multilevel dynamic factor model (TVP-MDFM) to improve the model’s accuracy for evaluating the dynamic policy effect. The estimation is based on the Bayes...
Article
Learning from label proportions (LLP) is a widespread and important learning paradigm: only the bag-level proportional information of the grouped training instances is available for the classification task, instead of the instance-level labels in the fully supervised scenario. As a result, LLP is a typical weakly supervised learning protocol and co...
Article
Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction...
Article
In the context of big data, organizations and individuals can often benefit from the data mining techniques, such as classification. However, decision-makers must quickly react to insights over time under dynamic environments. In this paper, we present a novel perspective, named concept-cognitive computing system (C3S), to achieve dynamic classific...
Preprint
Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structures cohered in networks. To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders. Under this framework, the problem of...
Preprint
Full-text available
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem...
Article
Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient...
Article
As one of the important dimensionality reduction techniques, unsupervised feature selection (UFS) has enjoyed amounts of popularity over the last few decades, which can not only improve learning performance, but also enhance interpretability and reduce computational costs. The existing UFS methods often model the data in the original feature space,...
Article
Learning from complementary labels (CLs) is a useful learning paradigm, where the CL specifies the classes that the instance does not belong to, instead of providing the ground truth as in the ordinary supervised learning scenario. In general, although it is less laborious and more efficient to collect CLs compared with ordinary labels, the less in...
Article
Relation Extraction (RE) aims at extracting meaningful relation facts between entities in texts. It is an important semantic processing task in the field of natural language processing (NLP) and has many applications. Traditional RE focuses on extracting entity relationships from a single input sentence. Recently, the research scope has been extend...
Article
A smart city plays an increasing role in citizens' daily life. A smart city has six main components, namely smart mobility, smart economy, smart governance, smart living, smart environment, and smart people. For the past several decades, government and organizations throughout the world have initiated many smart city projects. Citizens' transportat...
Article
Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpus annotated with Knowledge Graph to reduce the cost of acquisition. Nevertheless, the data noise pro...
Article
Single Image Super-Resolution (SISR) is a fundamental and important low-level computer vision (CV) task, yet its performance on real-world applications is not always satisfactory. Different from the previous SISR research, we focus on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) images, which...
Preprint
Full-text available
Automatic detecting anomalous regions in images of objects or textures without priors of the anomalies is challenging, especially when the anomalies appear in very small areas of the images, making difficult-to-detect visual variations, such as defects on manufacturing products. This paper proposes an effective unsupervised anomaly segmentation app...
Article
In recent years, deep-based models have achieved great success in the field of single image super-resolution (SISR), where tremendous parameters are always needed to obtain a satisfying performance. However, the high computational complexity extremely limits its applications to some mobile devices that possess less computing and storage resources....
Article
Recent interests in graph neural networks (GNNs) have received increasing concerns due to their superior ability in the network embedding field. The GNNs typically follow a message passing scheme and represent nodes by aggregating features from neighbors. However, the current aggregation methods assume that the network structure is static and defin...
Article
Full-text available
Most of existing e-commerce recommender systems have been designed to recommend the right products to users, based on the history of previous users’ individual transaction records. The real application scenarios of recommendation also have different requirements. From the customer point of view, many users visit the websites anonymously, so a pract...
Chapter
With the continuous improvement of data processing capabilities and storage capabilities, Big Data Era has entered the public sight. Under such a circumstance, the generation of massive data has greatly facilitated the development of data mining algorithms. This paper describes the status of data mining and presents three of our works: optimization...
Preprint
Full-text available
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly en...
Preprint
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly en...
Conference Paper
Full-text available
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures — edge and gradient, thus implicitly enco...
Article
Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article e...
Article
Corporate bankruptcy prediction is an interesting and important research topic that can be conceived in many practical applications. Recently, machine learning based methods have been widely proposed to solve the problem of bankruptcy prediction. However, the existing models do not consider that large amounts of instance-level labeled training data...
Article
This research article uses a Fuzzy Cognitive Map (FCM) approach to improve an earlier proposed IQ test characteristics of Artificial Intelligence (AI) systems. The defuzzification process makes use of fuzzy logic and the triangular membership function along with linguistic term analyses. Each edge of the proposed FCM is assigned to a positive or ne...
Conference Paper
Full-text available
Convolutional neural networks have been shown successful in extracting features from images and texts. However, it is difficult to apply convolutional neural networks directly on ubiquitous graph data since the graph data lies in an irregular structure. A significant number of researchers engrossed themselves in studying graph convolutional network...
Preprint
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devic...
Preprint
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism, without i...
Article
Full-text available
Recently, supervised deep super-resolution (SR) networks have achieved great success in both accuracy and texture generation. However, most methods train in the dataset with a fixed kernel (such as bicubic) between high-resolution images and their low-resolution counterparts. In real-life applications, pictures are always disturbed with additional...
Article
Learning with label proportions (LLP) is a weakly supervised learning problem that is conceivable in many real-world applications, where the training data is given in bags of instances, and only knowing the proportions of data points belonging to a particular category for each bag. However, how to effectively address the LLP problem with high dimen...
Chapter
As a core task and important part of Information ExtractionEntity Relation Extraction can realize the identification of the semantic relation between entity pairs. And it plays an important role in semantic understanding of sentences and the construction of entity knowledge base. It has the potential of employing distant supervision method, end-to-...
Article
Full-text available
Learning from label proportions (LLP) is a new kind of learning problem which has attracted wide interest in machine learning. Different from the well-known supervised learning, the training data of LLP is in the form of bags and only the proportion of each class in each bag is available. Actually, many modern applications can be successfully abstr...
Article
Ordinal regression is a special kind of machine learning problem, which aims to label patterns with an ordinal scale. Due to the ubiquitous existence of the ordering information in many practical cases, ordinal regression has received much attention and can be found in a great variety of applications. Meanwhile, Kernel Extreme Learning Machine (KEL...
Article
Unsupervised feature learning via auto-encoders results in low-dimensional representations in latent space that capture the patterns of input data. The auto-encoders with robust regularization learn qualified features that are less sensitive to small perturbations of inputs. However, the previous robust autoencoders highly depend on pre-defined str...
Article
Full-text available
Learning from label proportions is a new kind of learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, it considers instances in bags and uses the label proportion of each bag instead of instance. As obtaining the instance label is not always feasible, it has been widely used in areas li...
Article
Full-text available
Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general...
Article
Full-text available
Smart city is a solution for urban planning. The urban population would grow to 6.4 billion by 2050. Smart city is an efficient planning method for future cities. Smart city uses data and technology to improve citizen’s lives and business. Smart city leaders are playing an insightful role in guiding other smart city followers. This research uses th...
Article
Full-text available
Marketing performance measurement is important in retail companies, it is critical for the budget planning and adjustment of marketing strategies and tactics over time. Compared to digital online marketing, such as social media advertisers where tagging techniques [1] can be applied to track the lifecycle of the consumption and the customer’s behav...
Article
Full-text available
Bankruptcy prediction has long been a significant issue in finance and management science, which attracts the attention of researchers and practitioners. With the great development of modern information technology, it has evolved into using machine learning or deep learning algorithms to do the prediction, from the initial analysis of financial sta...
Article
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With the gradual acceleration of urbanization, Smart City has become a new trend of global urban development. From the perspective of urban residents and based on Maslow’s hierarchy of needs theory, this paper establishes an evaluation index system of smart cities based on residents’ needs, which includes five levels and 21 indicators. An evaluatio...
Preprint
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused on this kind of problems. Nonp...
Article
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes. Second, in highly biased networks, random walks are likely to bias to high-degree nodes and neglect the g...
Chapter
Full-text available
Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Traditional performance appraisal programs are labor-based, lacking of fairness. Furthermore, as globalization and technology advance, in order to meet...
Conference Paper
Full-text available
Multimedia data available in various disciplines are usually heterogeneous, containing representations in multi-views, where the cross-modal search techniques become necessary and useful. It is a challenging problem due to the heterogeneity of data with multiple modalities, multi-views in each modality and the diverse data categories. In this paper...
Chapter
Learning graph representations generally indicate mapping the vertices of a graph into a low-dimension space, in which the proximity of the original data can be preserved in the latent space. However, traditional methods that based on adjacent matrix suffered from high computational cost when encountering large graphs. In this paper, we propose a d...
Preprint
Full-text available
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node sequences changes. Second, in disequilibrium networks such as highly biases networks, random walk met...
Article
For a classification problem, the nonlinearly separable case can be transformed into a linearly separable one by using the kernel trick, However, introducing the single kernel function to a classifier can cause the difficulty of feature selection and it reduces the model interpretability, which is very important for some practical applications. Mor...
Article
Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Recently, with the development of sparsity research, both theoretical and empirical studies have suggested that the sparsity is one of the intrinsic properties of real world data and sparsity regularizatio...
Article
Full-text available
Large-scale problems have been a very active topic in machine learning area. In the time of big data, it is a challenge and meaningful work to solve such problems. Standard SVM can make linear classification on large-scale problems effectively, with acceptable training time and excellent prediction accuracy. However, nonparallel SVM (NPSVM) and ram...
Article
Learning from label proportions (LLP), in which the training data is in form of bags and only the proportion of each class in each bag is available, has attracted wide interest in machine learning. However, how to solve high-dimensional LLP problem is still a challenging task. In this paper, we propose a novel algorithm called learning from label p...
Article
Full-text available
Feature selection aims to select a subset of features from high-dimensional data according to a predefined selecting criterion. Sparse learning has been proven to be a powerful technique in feature selection. Sparse regularizer, as a key component of sparse learning, has been studied for several years. Although convex regularizers have been used in...
Article
Full-text available
This research paper we present a Fuzzy Cognitive Map (FCM)-based approach to improving a previously proposed IQ test for Artificial Intelligence (AI) systems. Starting from linguistic terms analyses, fuzzy logic along with triangular membership function is adopted for the defuzzification process. Based on the defuzzification result, a calculated de...
Article
Full-text available
Data mining has been an active research area for a couple of decades, yet the complicated nature of data mining is still not fully understood. One common misunderstanding of data mining is: Give me the data set, and data mining tools will show me the hidden knowledge. However, this thinking is quite naive, and is not realistic in many real world ap...
Book
The three-volume set LNCS 10860, 10861 + 10862 constitutes the proceedings of the 18th International Conference on Computational Science, ICCS 2018, held in Wuxi, China, in June 2018. The total of 155 full and 66 short papers presented in this book set was carefully reviewed and selected from 404 submissions. The papers were organized in topical se...
Book
The three-volume set LNCS 10860, 10861 and 10862 constitutes the proceedings of the 18th International Conference on Computational Science, ICCS 2018, held in Wuxi, China, in June 2018. The total of 155 full and 66 short papers presented in this book set was carefully reviewed and selected from 404 submissions. The papers were organized in topical...
Book
The three-volume set LNCS 10860, 10861 and 10862 constitutes the proceedings of the 18th International Conference on Computational Science, ICCS 2018, held in Wuxi, China, in June 2018. The total of 155 full and 66 short papers presented in this book set was carefully reviewed and selected from 404 submissions. The papers were organized in topical...
Article
Full-text available
Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is c...
Article
Full-text available
Image segmentation is an important and fundamental task in computer vision. Its performance is mainly influenced by feature representations and segmentation algorithms. In this paper, we propose a novel clustering-based image segmentation approach which can be called ICDP algorithm. It is able to capture the inherent structure of image and detect t...