Search

Abstract Sentiment analysis of social media data is crucial to understand people's position, attitude, and opinion toward a certain event, which has many applications such as election prediction and product evaluation. Though great effort has been devoted to the single modality (image or text), less effort is paid to the joint analysis of multimodal data in social media. Most of the existing methods for multimodal sentiment analysis simply combine different data modalities, which results in dissatisfying performance on sentiment classification. In this paper, we propose a novel image–text sentiment analysis model, i.e., Deep Multimodal Attentive Fusion (DMAF), to exploit the discriminative features and the internal correlation between visual and semantic contents with a mixed fusion framework for sentiment analysis. Specifically, to automatically focus on discriminative regions and important words which are most related to the sentiment, two separate unimodal attention models are proposed to learn effective emotion classifiers for visual and textual modality respectively. Then, an intermediate fusion-based multimodal attention model is proposed to exploit the internal correlation between visual and textual features for joint sentiment classification. Finally, a late fusion scheme is applied to combine the three attention models for sentiment prediction. Extensive experiments are conducted to demonstrate the effectiveness of our approach on both weakly labeled and manually labeled datasets. [ABSTRACT FROM AUTHOR]

Recently, a large number of geo-tagged landmark images have been uploaded through various social media services. Usually, these geo-tagged images are annotated by users with GPS and tags related to the landmarks where they are taken. Landmark tagging aims to automatically annotate an image with the tags to describe the landmark where the image is taken. It has been observed that the images and tags show strong correlation with the geographical locations. The widely used assumption by many existing tagging methods is that images are independently and identically distributed is not effective to capture the geographical correlation. In this paper, we study the novel problem of utilizing the geographical correlation among images and landmarks for better tagging landmark images. In particular, we propose an unsupervised feature learning approach to learn the geographically discriminative features across geographical locations, by integrating latent space learning and geographically structural analysis (LSGSA) into a joint model. A latent space learning model is proposed to effectively fuse the heterogeneous features of visual content and tags. Meanwhile, the geographical structure analysis and group sparsity are applied to learn the geographically discriminative features. Then, a geo-guided sparse reconstruction method is proposed to tag images by utilizing the discriminative information of features, in which the landmark-specific tags are boosted by a weighting method. Experiments on the real-world datasets demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

This paper studies response selection for human-computer conversation systems. Existing retrieval-based human-computer conversation systems are intended to reply to user utterances based on existing utterance-response pairs. However, collecting sufficient utterance-response pairs is intractable in practical situations, especially for many specific domains. We introduce DocChat a novel information retrieval approach for human-computer conversation systems that can use unstructured documents rather than semi-structured utterance-response pairs, to react to user utterances. The key of DocChat is a learning to rank model with features designed at various levels of granularity which is proposed to quantify the relevance between utterances and responses directly. We conduct comprehensive experiments on both sentence selection and real human-computer conversation scenarios. Empirical studies of sentence selection datasets shows reasonable improvements and the strong adaptability of our model. We compare DocChat with Xiaoice, a famous open domain chitchat engine in China. Side-by-side evaluation shows that DocChat is a good complement for human-computer conversation systems using utterance-response pairs as the primary source of responses. Furthermore, we release a large scale open-domain dataset for sentence selection which contains 304,413 query-sentence pairs. [ABSTRACT FROM AUTHOR]

Unsupervised extractive summarization aims to extract salient sentences from the document without labeled corpus. Existing methods have achieved promising progress, thanks to the power of large-scale pre-trained language models and high-quality contextualized representations. However, extractive summaries often fail to maintain smooth transitions between sentences and struggle to form a coherent and fluent text due to splicing of sentences. Nevertheless, to the best of our knowledge, very few studies currently focus on unsupervised abstractive summarization. Inspired by the intuitive human process of writing summaries, which involves extracting salient sentences first and then reconstructing them, in this paper, we propose an Extract-then-Abstract framework to generate more coherent and human-like summary. Specifically, we first adopt extractive summarization model as summarizer to generate extractive summary in the extraction stage. Then in the abstraction stage, we propose a BART-based sentence write model to generate more coherent and fluent abstractive summary. To this end, we design a novel parallel data creation method for our rewrite model by proposing an effective sentence sampling strategy without any manual annotation cost. Extensive experiments including automatic evaluation and human evaluation demonstrate that our framework consistently outperforms strong baselines for unsupervised abstractive summarization and can generate more coherent and human-like summary while maintaining in competitive ROUGE scores for unsupervised extractive summarization. [ABSTRACT FROM AUTHOR]

• A novel token selection mechanism based on the dynamic self-attention network is proposed to automatically extract important video features. • A vision synchronization network is proposed to align appearance and motion features at the time slice level. • Extensive experiments and analysis confirm the superiority of the proposed model DSAVS. Video Question Answering (VideoQA) has gained increasing attention as an important task in understanding the rich spatio-temporal contents, i.e., the appearance and motion in the video. However, existing approaches mainly use the question to learn attentions over all the sampled appearance and motion features separately, which neglect two properties of VideoQA: (1) the answer to the question is often reflected on a few frames and video clips, and most video contents are superfluous; (2) appearance and motion features are usually concomitant and complementary to each other in time series. In this paper, we propose a novel VideoQA model, i.e., Dynamic Self-Attention with Vision Synchronization Networks (DSAVS), to address these problems. Specifically, a gated token selection mechanism is proposed to dynamically select the important tokens from appearance and motion sequences. These chosen tokens are fed into a self-attention mechanism to model the internal dependencies for more effective representation learning. To capture the correlation between the appearance and motion features, a vision synchronization block is proposed to synchronize the two types of vision features at the time slice level. Then, the visual objects can be correlated with their corresponding activities and the performance is further improved. Extensive experiments conducted on three public VideoQA data sets confirm the effectivity and superiority of our model compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Smart substation is a crucial Cyber-Physical system and is prone to cyber-attack. In this paper, we propose a novel anomaly detection mechanism tailored for detecting the IEC 61850-based network traffic. Three types of traffic features are taken into account for comprehensively characterizing the network traffic during a time window. To eliminate the subjectivity of manually selecting the traffic features, we exploit Discrete Wavelet Transform (DWT) algorithm to secondarily extract the deep features. An improved Locally Linear Embedding (LLE) algorithm is proposed to reduce the dimension of deep feature vectors with more effective dimensionality reduction ability. By doing so, the LSTM (Long Short Term Memory)-based Autoencoder network that can learn to reconstruct the normal traffic time-series behavior, and thereafter uses the reconstruction error to detect the anomalies. We assess the performance of our proposed mechanism with the comprehensive experiments on the real smart substation. The results indicate that the proposed mechanism can be performed in a fast way with satisfactory detection performance. [ABSTRACT FROM AUTHOR]

As one of the most influential social media platforms, microblogging is becoming increasingly popular in the last decades. Each day a large amount of events appear and spread in microblogging. The spreading of events and corresponding comments on them can greatly influence the public opinion. It is practical important to discover new emerging events in microblogging and predict their future popularity. Traditional event detection and information diffusion models cannot effectively handle our studied problem, because most existing methods focus only on event detection but ignore to predict their future trend. In this paper, we propose a new approach to detect burst novel events and predict their future popularity simultaneously. Specifically, we first detect events from online microblogging stream by utilizing multiple types of information, i.e., term frequency, and user׳s social relation. Meanwhile, the popularity of detected event is predicted through a proposed diffusion model which takes both the content and user information of the event into account. Extensive evaluations on two real-world datasets demonstrate the effectiveness of our approach on both event detection and their popularity prediction. [ABSTRACT FROM AUTHOR]

The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommendation. However, currently researches on GCN models usually recursively aggregate the information from all the neighbors or randomly sampled neighbor subsets, without explicitly identifying whether the aggregated neighbors provide useful information during the graph convolution. In this paper, we theoretically analyze the affection of the neighbor quality over GCN models' performance and propose the Neighbor Enhanced Graph Convolutional Network (NEGCN) framework to boost the performance of existing GCN models. Our contribution is three-fold. First, we at the first time propose the concept of neighbor quality for both node classification and recommendation tasks in a general theoretical framework. Specifically, for node classification, we propose three propositions to theoretically analyze how the neighbor quality affects the node classification performance of GCN models. Second, based on the three proposed propositions, we introduce the graph refinement process including specially designed neighbor evaluation methods to increase the neighbor quality so as to boost both the node classification and recommendation tasks. Third, we conduct extensive node classification and recommendation experiments on several benchmark datasets. The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks. [ABSTRACT FROM AUTHOR]

Abstract: Online social network has become a popular way for users to express themselves, connect and share information with each other. However, in online social networks, the connections between different users are all in binary status, which neglects the relationship strengths between them. Meanwhile, the relationship strength between different users is activity field specific. In different activity fields, such as traveling, shopping, and sport, the relationship strengths between the same users may vary significantly. Therefore, in this paper we propose a general framework to measure the relationship strengths between different users, taking consideration not only the user''s profile information but also the interaction activities and the activity fields. We conduct the experiments on Facebook dataset and the results show that the proposed framework is promising and can be used to improve the performances of various applications. [Copyright &y& Elsevier]

• A novel model based on the self-attention mechanism is proposed to learn more effective multi-modal representations. • The DSACA model is proposed to capture the internal dependencies and cross-modal correlation between the image and question sentence. • Extensive experiments and analysis confirm the superiority of the proposed DSACA. Visual Question Answering (VQA) as an important task in understanding vision and language has been proposed and aroused wide interests. In previous VQA methods, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are generally used to extract visual and textual features respectively, and then the correlation between these two features is explored to infer the answer. However, CNN mainly focuses on extracting local spatial information and RNN pays more attention on exploiting sequential architecture and long-range dependencies. It is difficult for them to integrate the local features with their global dependencies to learn more effective representations of the image and question. To address this problem, we propose a novel model, i.e., Dual Self-Attention with Co-Attention networks (DSACA), for VQA. It aims to model the internal dependencies of both the spatial and sequential structure respectively by using the newly proposed self-attention mechanism. Specifically, DSACA mainly contains three submodules. The visual self-attention module selectively aggregates the visual features at each region by a weighted sum of the features at all positions. The textual self-attention module automatically emphasizes the interdependent word features by integrating associated features among the sentence words. Besides, the visual-textual co-attention module explores the close correlation between visual and textual features learned from self-attention modules. The three modules are integrated into an end-to-end framework to infer the answer. Extensive experiments performed on three generally used VQA datasets confirm the favorable performance of DSACA compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To meet the requirements of energy-efficiency and high quality joining in the emerging industries field, a novel thermal explosion (TE) joining method was developed. The dissimilar Ni/316SS joint was first obtained by the TE reaction technology. The Ni-Al mixed powder was selected as TE reaction system, and the reaction product was used as interlayer. The effect of temperature (800, 850 and 900 °C), hold time (5, 10, 15 and 20 min) and joining pressure (0, 10, 20 and 30 MPa) on interfacial microstructure, diffusion behavior and mechanical properties of Ni/316SS joint were investigated. The maximum combustion temperature of Ni-Al interlayer during heating process was 1655 °C and a significant exothermic phenomenon was lasted for 2 s. 316SS(Al), Fe 2 Al 5 (Ni, Cr) and serration-like Ni 5 Al 3 and Ni 3 Al were detected at the interface containing a reaction layer because of element diffusion. The maximum shear strength of the Ni/316SS joint was 65.2 MPa, and the failure is characterized by typical brittleness fracture. The fracture mode is the combination of partial spalling and fracture of the interlayer. The post heat-treatment assisted TE joining technique significantly shortened the bonding process of the dissimilar Ni/316SS joint at moderate temperatures, realizing the low energy and efficient joining between Ni and 316SS. [ABSTRACT FROM AUTHOR]

• Define three types of features extracted from URLs, domains, etc. • Exploit a method to balance the majority and minority class samples. • Adopt an improved DAE-based method to reduce the dimension of the dataset. • Boost the detection performance by using the improved ELM-based classifier. • Do experiments to verify the feasibility and effectiveness of the proposed approach. In this paper, a novel approach based on non-inverse matrix online sequence extreme learning machine (NIOSELM) for phishing detection is presented, which takes into account three types of features to comprehensively characterize a website. For the NIOSELM algorithm, we use Sherman Morriso Woodbury equation to avoid the matrix inversion operation, and introduce the idea of online sequence extreme learning machine (OSELM) to update the training model. In order to reduce the dependence of the detection model on the majority class, we use Adaptive Synthetic Sampling (ADASYN) algorithm to generate the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. Furthermore, an improved denoising auto-encoder (SDAE) is designed to reduce the dimension of the experimental dataset. The experimental results show the efficiency and feasibility of the proposed detection mechanism. Moreover, the overall detection performance of NIOSELM is better than that of other existing methods, especially in training speed and the detection accuracy. [ABSTRACT FROM AUTHOR]

Visual Question Answering (VQA) has emerged and aroused widespread interest in recent years. Its purpose is to explore the close correlations between the image and question for answer inference. We have two observations about the VQA task: (1) the number of newly defined answers is ever-growing, which means that answer prediction on pre-defined labeled answers may lead to errors, as some unlabeled answers may be the right choice to the question–image pairs; (2) in the process of answering visual questions, the gradual change of human attention has an important guiding role in exploring the correlations between images and questions. Based on these observations, we propose a novel model for VQA, i.e., combining Inferential Attention and Semantic Space Mapping (IASSM). Specifically, our model has two salient aspects: (1) a semantic space shared by both the labeled and unlabeled answers is constructed to learn new answers, where the joint embedding of a question and the corresponding image is mapped and clustered around the answer exemplar; (2) a novel inferential attention model is designed to simulate the learning process of human attention to explore the correlations between the image and question. It focuses on the more important question words and image regions associated with the question. Both the inferential attention and the semantic space mapping modules are integrated into an end-to-end framework to infer the answer. Experiments performed on two public VQA datasets and our newly constructed dataset show the superiority of IASSM compared with existing methods. [ABSTRACT FROM AUTHOR]

Graph-based semi-supervised learning (SSL) has been widely investigated in recent works considering its powerful ability to naturally incorporate the diverse types of information and measurements. However, traditional graph-based SSL methods have cubic complexities and leading to low scalability. In this paper, we propose to perform graph-based SSL on mixture distribution components, named Mixture-distribution based Graph Smoothing (MGS), to address this challenge. Specifically, the intrinsic distributions of data are captured by a mixture density estimation model. A novel mixture-distribution based objective energy function is further proposed to incorporate few available annotations, which ensures the model complexity is irrelevant to the number of raw instances. The energy function can be simplified and effectively solved by viewing the instances and mixture components as the point clouds. Experiments on large datasets demonstrate the remarkable performance improvements and scalability of the proposed model, which proves the superiority of the MGS model. • We propose a novel scalable SSL, which overcomes the limitations of existing methods. • We extend our method to a more general framework for anchor-based scalable SSL. • The experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Social network alignment, aiming at linking identical identities across different social platforms, is a fundamental task in social graph mining. Most existing approaches are supervised models and require a large number of manually labeled data, which are infeasible in practice considering the yawning gap between social platforms. Recently, isomorphism across social networks is incorporated as complementary to link identities from the distribution level, which contributes to alleviating the dependency on sample-level annotations. Adversarial learning is adopted to learn a shared projection function by minimizing the distance between two social distributions. However, the hypothesis of isomorphism might not always hold true as social user behaviors are generally unpredictable, and thus a shared projection function is insufficient to handle the sophisticated cross-platform correlations. In addition, adversarial learning suffers from training instability and uncertainty, which may hinder model performance. In this article, we propose a novel meta-learning-based social network alignment model Meta-SNA to effectively capture the isomorphism and the unique characteristics of each identity. Our motivation lies in learning a shared meta-model to preserve the global cross-platform knowledge and an adaptor to learn a specific projection function for each identity. Sinkhorn distance is further introduced as the distribution closeness measurement to tackle the limitations of adversarial learning, which owns an explicitly optimal solution and can be efficiently computed by the matrix scaling algorithm. Empirically, we evaluate the proposed model over multiple datasets, and the experimental results demonstrate the superiority of Meta-SNA.

The analysis of large-scale multimodal data has become very popular recently. Image captioning, whose goal is to describe the content of image with natural language automatically, is an essential and challenging task in artificial intelligence. Commonly, most existing image caption methods utilize the mixture of Convolutional Neural Network and Recurrent Neural Network framework. These methods either pay attention to global representation at the image level or only focus on the specific concepts, such as regions and objects. To make the most of characteristics about a given image, in this study, we present a novel model named Multilevel Attention Networks and Policy Reinforcement Learning for image caption generation. Specifically, our model is composed of a multilevel attention network module and a policy reinforcement learning module. In the multilevel attention network, the object-attention network aims to capture global and local details about objects, whereas the region-attention network obtains global and local features about regions. After that, a policy reinforcement learning algorithm is adopted to overcome the exposure bias problem in the training phase and solve the loss-evaluation mismatching problem at the caption generation stage. With the attention network and policy algorithm, our model can automatically generate accurate and natural sentences for any particular image. We carry out extensive experiments on the MSCOCO and Flickr30k data sets, demonstrating that our model is superior to other competitive methods.

Visual question answering (VQA) has gained increasing attention in both natural language processing and computer vision. The attention mechanism plays a crucial role in relating the question to meaningful image regions for answer inference. However, most existing VQA methods: 1) learn the attention distribution either from free-form regions or detection boxes in the image, which is intractable in answering questions about the foreground object and background form, respectively and 2) neglect the prior knowledge of human attention and learn the attention distribution with an unguided strategy. To fully exploit the advantages of attention, the learned attention distribution should focus more on the question-related image regions, such as human attention for both the questions, about the foreground object and background form. To achieve this, this article proposes a novel VQA model, called adversarial learning of supervised attentions (ALSAs). Specifically, two supervised attention modules: 1) free form-based and 2) detection-based, are designed to exploit the prior knowledge for attention distribution learning. To effectively learn the correlations between the question and image from different views, that is, free-form regions and detection boxes, an adversarial learning mechanism is implemented as an interplay between two supervised attention modules. The adversarial learning reinforces the two attention modules mutually to make the learned multiview features more effective for answer inference. The experiments performed on three commonly used VQA datasets confirm the favorable performance of ALSA.

These days, social media users tend to express their feelings through sharing images online. Capturing the emotions embedded in these social images involves great research challenges and practical values. Most existing works concentrate on extracting the visual feature from a global view, while ignoring the fact that visual objects are also rich in emotion. How to leverage the multilevel visual features to improve the sentiment analysis performance is important yet challenging. Besides, existing works view each social image as an independent sample while ignoring the rich correlations among social images, which may be helpful in detecting visual emotion. In this article, we propose a novel model called social relations-guided multiattention networks (SRGMANs) to incorporate both the multilevel (region-level and object-level) visual features of a single image and the correlations among multiple social images to conduct visual sentiment analysis. Specifically, we first construct a heterogeneous network consisting of various types of social relations and introduce a heterogeneous network embedding method to learn the network representation for each image. Then, two visual attention branches (region attention network and object attention network) are devised to extract emotional and discriminative visual features. For each branch, we design a self-attention module to capture the emotional dependencies among visual parts. Besides, a network-guided attention module is also designed in each branch to focus on more network-related emotional visual parts with the guidance of the topology information. Finally, the attended visual features from the two attention models, together with network representation features, are combined within a holistic framework to predict the sentiment of social images. Extensive experiments demonstrate the superiority of our model on three benchmark datasets.

Learning the representation for social images has recently made remarkable achievements for many tasks, such as cross-modal retrieval and multilabel classification. However, since social images contain both multimodal contents (e.g., visual images and textual descriptions) and social relations among images, simply modeling the content information may lead to suboptimal embedding. In this paper, we propose a novel multimodal representation learning model for social images, that is, correlational multimodal variational autoencoder (CMVAE) via triplet network. Specifically, in order to mine the highly nonlinear correlation between the visual content and the textual content, a CMVAE is proposed to learn a unified representation for the multiple modalities of social images. Both common information in all modalities and private information in each modality are encoded for the representation learning. To incorporate the social relations among images, we employ the triplet network to embed multiple types of social links in the representation learning. Then, a joint embedding model is proposed to combine the social relations for representation learning of the multimodal contents. Comprehensive experiment results on four datasets confirm the effectiveness of our method in two tasks, namely, multilabel classification and cross-modal retrieval. Our method outperforms the state-of-the-art multimodal representation learning methods with significant improvement of performance.

Metaldehyde has been detected in drinking water system in relatively high concentration exceeding European water quality standard. In order to address this problem, the aim of this project was to treat metaldehyde aqueous solution by advanced oxidation processes (AOPs) and granular activated carbon (GAC) column. Ten novel materials were tested for degradation rates of metaldehyde under ultraviolet light irradiation (UVC). For treatment of 1 mg/L metaldehyde solution by AOPs, the highest degradation rate is 16.59% under UVC light with the aid of nitrogen doped titanium dioxide coated graphene (NTiO₂/Gr). Furthermore, 0.5 mg/L is the optimal concentration for degradation of metaldehyde with N-TiO₂/Gr under UVC light. Apart from that, the lifetime of GAC column could be elongated on condition that metaldehyde has been treated by AOPs previously. Hence, combination of AOPs and GAC column is promising in treating water containing metaldehyde.

Image-text matching by deep models has recently made remarkable achievements in many tasks, such as image caption and image search. A major challenge of matching the image and text lies in that they usually have complicated underlying relations between them and simply modeling the relations may lead to suboptimal performance. In this paper, we develop a novel approach Bi-directional Spatial-Semantic Attention Networks (BSSAN), which leverages both the word to regions (W2R) relation and image object to words (O2W) relation in a holistic deep framework for more effectively matching. Specifically, to effectively encode the W2R relation, we adopt LSTM with bilinear attention function to infer the image regions which are more related to the particular words, which is referred as the W2R attention network. On the other side, the O2W attention network is proposed to discover the semanticallyclose words for each visual object in the image, i.e., the visual object to words (O2W) relation. Then a deep model unifying both of the two directional attention networks into a holistic learning framework is proposed to learn the matching scores of image and text pairs. Compared to existing image-text matching methods, our approach achieves state-of-the-art performance on the datasets of Flickr30K and MSCOCO.

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

Background: Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets., Results: This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions., Conclusions: The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.

Background: In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists., Results: In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track., Conclusions: The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.