Graph neural networks (GNNs) are prominent for their effectiveness in processing graph-structured data for semi-supervised node classification tasks. Most existing GNNs perform message passing directly based on the observed graph structure. However, in real-world scenarios, the observed structure is often suboptimal due to multiple factors, significantly degrading the performance of GNNs. To address this challenge, we first conduct an empirical analysis showing that different graph structures significantly impact empirical risk and classification performance. Motivated by our observations, we propose a novel method named Trade-off Graph Structure Learning (TGSL), guided by the multifaceted Graph Information Bottleneck (GIB) principle based on Mutual Information (MI). The key idea behind TGSL is to learn a minimal sufficient graph structure that minimizes empirical risk while maintaining performance. Specifically, we introduce global feature augmentation to capture the structural roles of nodes, and global structure augmentation to uncover global relationships between nodes. The augmented graphs are then processed by structure estimators with different parameters for refinement and redefinition, respectively. Additionally, we innovatively leverage multifaceted GIB as the optimization objective by maximizing the MI between the labels and the representation derived from the final structure, while constraining the MI between this representation and that based on the redefined structures. This trade-off helps avoid capturing irrelevant information from the redefined structures and enhances the final representation for node classification. We conduct extensive experiments across a range of datasets under clean and attacked conditions. The results demonstrate the outstanding performance and robustness of TGSL over state-of-the-art baselines.

Neural rendering techniques have made substantial progress in generating photo-realistic 3D scenes. The latest 3D Gaussian Splatting technique has achieved high quality novel view synthesis as well as fast rendering speed. However, 3D Gaussians lack proficiency in defining accurate 3D geometric structures despite their explicit primitive representations. This is due to the fact that Gaussian’s attributes are primarily tailored and fine-tuned for rendering diverse 2D images by their anisotropic nature. To pave the way for efficient 3D reconstruction, we present Spherical Gaussians, a simple and effective representation for 3D geometric boundaries, from which we can directly reconstruct 3D feature curves from a set of calibrated multi-view images. Spherical Gaussians is optimized from grid initialization with a view-based rendering loss, where a 2D edge map is rendered at a specific view and then compared to the ground-truth edge map extracted from the corresponding image, without the need for any 3D guidance or supervision. Given Spherical Gaussians serve as intermedia for the robust edge representation, we further introduce a novel optimization-based algorithm called SGCR to directly extract accurate parametric curves from aligned Spherical Gaussians. We demonstrate that SGCR outperforms existing state-of-the-art methods in 3D edge reconstruction while enjoying great efficiency. Code is available at https://github.com/Martinyxr/SGCR.

Point cloud reconstruction is a critical process in 3D representation and reverse engineering. When it comes to CAD models, edges are significant features that play a crucial role in characterizing the geometry of 3D shapes. However, few points are exactly sampled on edges during acquisition, resulting in apparent artifacts for the reconstruction task. Upsampling point cloud is a direct technical route, but there is a main challenge that the upsampled points may not align with the model edge accurately. To overcome this, we develop an integrated framework to estimate edges by joint regression of three geometry features—point-to-edge direction, point-to-edge distance and point normal. Benefiting these features, we implement a novel refinement process to move and produce more points which lie accurately on edges of the model, allowing for high-quality edge-preserving reconstruction. Experiments and comparisons against previous methods demonstrate our method’s effectiveness and superiority.

Graph neural networks (GNNs) have proven highly effective in handling graph-structured data. However, most existing GNNs rely on the homophily assumption, hindering their performance on heterophilic graphs. This limitation is partially due to aggregation containing irrelevant nodes. In this work, we propose a novel GNN model based on node correlation called NoC-GNN, to address the deficiencies of existing techniques. NoC-GNN retains relevant nodes while removing irrelevant nodes, enhancing the effectiveness of nodes in the mixed state. NoC-GNN first constructs a new graph structure based on the k-nearest neighbor (kNN) graph to aggregate relevant nodes, and then constructs a matrix based on the new graph structure to remove possibly irrelevant nodes. Finally, the attention mechanism is used to adaptively integrate relevant, irrelevant, and self-information to model both homophilic and heterophilic graphs. Experimental results demonstrate that NoC-GNN achieves superior performance across a wide range of semi-supervised node classification tasks.

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to the potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC

Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly. Most GNNs assume that the networks are homophilous, which is not always true in the real world, i.e., structure noise or disassortative graphs. Only a few works focus on generalizing graph neural networks to heterophilous or low homophilous networks, where connected nodes may have different labels. In this paper, we design a simple and effective Graph Structure Learning strategy based on Feature and Label consistency (GSLFL) to increase the homophilous level of networks for generalizing any existing GNNs to heterophilous networks. Specifically, we first introduce a method to learn graph structure based on node features and then modify the graph structure based on label consistency. Further, we combine the GSLFL with three existing GNNs to learn node representations and graph structure together. And we design a self-training method to iteratively train models and modify graph structure with pseudo-labels. Finally, our empirical results on 6 public networks with homophily or heterophily, and structure attacks show that our methods outperform the state-of-the-art methods in most cases.

Graph Neural Networks (GNNs) have achieved significant success in handling graph-structured data, such as knowledge graphs, citation networks, molecular structures, etc. However, most of them are usually shallow structures because of the over-smoothing problem that the representations of nodes are indistinguishable when stacking many layers. Several recent studies have tried to design deep GNNs for powerful expression ability by enlarging the receptive fields to aggregate information from high-order neighbors. But deep models may give rise to overfitting problem. In this paper, we propose a novel insight to aggregate more useful information based on multi-view which does not require deep structures. Specifically, we first design two complementary views to describe global topology and feature similarity of nodes. Then we devise an attention strategy to fuse node representations, named M ulti-V iew G raph C onvolutional N etowrk(MV-GCN). Further, we introduce a self-supervised technique to learn node representations by contrastive learning on different views, which can learn distinctive node embeddings from a large number of unlabeled data, named M ulti-V iew C ontrastive G raph C onvolutional Network(MV-CGC). Finally, we conduct extensive experiments on six public datasets for node classification, which prove the superiority of two proposed models compared with state-of-the-art methods.

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function pre-screen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate sub-cellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequence-based proteins into consideration, a novel boosting Dual-Modal Dual-Label (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dual-modal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as dual-label, which would be of great value as a pre-screen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of real-world channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with state-of-the-art algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effectiveness of hypothesis reuse mechanism in DMDL.

Recent work has achieved great success in utilizing global contextual information for semantic segmentation, including increasing the receptive field and aggregating pyramid feature representations. In this paper, we go beyond global context and explore the fine-grained representation using co-occurrent features by introducing Co-occurrent Feature Model, which predicts the distribution of co-occurrent features for a given target. To leverage the semantic context in the co-occurrent features, we build an Aggregated Co-occurrent Feature (ACF) Module by aggregating the probability of the co-occurrent feature with the co-occurrent context. ACF Module learns a fine-grained spatial invariant representation to capture co-occurrent context information across the scene. Our approach significantly improves the segmentation results using FCN and achieves superior performance 54.0% mIoU on Pascal Context, 87.2% mIoU on Pascal VOC 2012 and 44.89% mIoU on ADE20K datasets. The source code and complete system will be publicly available upon publication.

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendat ion and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification.

In multi-label classification settings, one of the most common problems is the massive label output space. To alleviate this, some methods opt to exploit label correlations to reduce the output space during prediction. However, these methods sacrifice efficiency or ignore global label correlations. In addition, label imbalances are another problem that is prevalent in multi-label classification. Current methods of correcting for imbalance oftentimes use single-label methods, which fail to consider label correlations. In this paper, we introduce general frameworks that incorporate topic modeling to seamlessly address both problems. We show that these frameworks can allow even the most naïve methods, such as Binary Relevance, to perform similarly to state-of-the-art methods. Furthermore, we show that our frameworks can also adapt state-of-the-art methods to perform better than the methods by themselves.

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

Inspired by recent works in Aspect-Based Sentiment Analysis (ABSA) on product reviews and faced with more complex posts on social media platforms mentioning multiple entities as well as multiple aspects, we define a novel task called Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA). This task aims at fine-grained sentiment analysis of (entity, aspect) combinations, making the well-studied ABSA task a special case of it. To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method. Our experimental results show that our CEA method achieves a significant gain over several baselines, including the state-of-the-art method for the ABSA task, and their enhanced versions, on datasets for ME-ABSA and ABSA tasks. The in-depth analysis illustrates the significant advantage of the CEA method over baseline methods for several hard-to-predict post types. Furthermore, we show that the CEA method is capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

Similarity Search has been studied in many different fields of computer science, including data mining, information retrieval, databases and so on. Document similarity self-join is a crucial part of lots of applications, such as near-duplicate document detection, document clustering and web search. On a collection of documents, document similarity self-join finds out all pairs of documents whose similarity values are no lower than a threshold value. However, similarity search is a computation-intensive procedure and consumes a large amount of time as the dataset size increases. Thus, many serial algorithms focus on speeding up the process by decreasing the possible similarity candidates for each query object on high-dimensional sparse datasets, including documents. However, the efficiency of those serial algorithms degrade badly as the threshold decreases. Parallel implementations based on OpenMP or MapReduce also adopt the pruning policy and do not solve the problem thoroughly. In this context, taking into account features of document datasets, we propose 2Step-SSJ, which solves the document similarity self-join in CUDA environment on GPUs. 2Step-SSJ performs the similarity self-join in two steps, i.e., similarity computing on the inverted list and similarity computing on the forward list, which compromises between the memory visiting and dot-product computation. The experimental results show that 2Step-SSJ could solve the problem much faster than existing methods on three benchmark text corpora, achieving the speedup of 2×-23× against the state-of-the-art parallel algorithm in general, while keep a relatively stable running time with different values of the threshold.

The demand for mining massive short-text data from the Internet has promoted researches on topic models. There exist many schemes trying to solve the sparsity problems brought by short texts, mainly based on data aggregation or model improvement. Among them, Biterm Topic Model changes the way of modeling topics, which is on document-level biterms and has shown creativity and effectiveness. However, this may ignore those semantically similar and rarely co-occurrent word pairs, which are denoted as global biterms in this paper. Inspired by the successful application of word embeddings in GPU-DMM, we exploit word embeddings to extract semantically similar word pairs from the whole corpus to help discover better topics. We call this model as GloSS, which takes advantages of both the approach to model topics and word embeddings. Experimental results on two open-source and real datasets are superior to state-of-the-art topic models for short texts.

In our daily life, short texts have been everywhere especially since the emergence of social network. There are countless short texts in online media like twitter, online Q&A sites and so on. Discovering topics is quite valuable in various application domains such as content recommendation and text characterization. Traditional topic models like LDA are widely applied for sorts of tasks, but when it comes to short text scenario, these models may get stuck due to the lack of words. Recently, a popular model named BTM uses word co-occurrence relationship to solve the sparsity problem and is proved effectively. However, both BTM and extended models ignore the inside relationship between words. From our perspectives, more related words should appear in the same topic. Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words. Experiments on two real-world short text datasets show great utility of our model.

Parkinson's disease is a debilitating and chronic disease of the nervous system. Traditional Chinese Medicine (TCM) is a new way for diagnosing Parkinson, and the data of Chinese Medicine for diagnosing Parkinson is a multi-label data set. Considering that the symptoms as the labels in Parkinson data set always have correlations with each other, we can facilitate the multi-label learning process by exploiting label correlations. Current multi-label classification methods mainly try to exploit the correlations from label pairwise or label chain. In this paper, we propose a simple and efficient framework for multi-label classification called Latent Dirichlet Allocation Multi-Label (LDAML), which aims at leaning the global correlations by using the topic model on the class labels. Briefly, we try to obtain the abstract “topics” on the label set by topic model, which can exploit the global correlations among the labels. Extensive experiments clearly validate that the proposed approach is a general and effective framework which can improve most of the multi-label algorithms' performance. Based on the framework, we achieve satisfying experimental results on TCM Parkinson data set which can provide a reference and help for the development of this field.

In multi-label learning, each object is represented by a single instance while associated with a set of class labels, and labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the correlations from label pairwise or label chain. In this paper, we propose a simple and efficient framework for multi-label classification, called Latent Dirichlet Allocation Multi-Label(LDAML), which aims at leaning the global correlations by using the topic model on the class labels. We regard the label set associated with each instance as a document and each class label in the label set as a word, then obtain the topic in the label space by topic model. The topics of label set which are introduced into feature space as the information of the correlations can improve the prediction. Extensive experiments clearly validate the effectiveness of the proposed approach.

Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved campaigns will produce their changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile nowadays successful online social networks are creating new media environment, however little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the “Social Influence inference of anti-Tobacco mass mEdia campaigns” (SITE) problem. To address the SITE problem, a novel influence inference framework, “TV Advertising Social Influence Estimation” (ASIE), is proposed based on our analysis of two anti-tobacco campaigns. ASIE divides audience attitudes towards TV ads into three distinct stages: (1) Cognitive, (2) Affective and (3) Conative. Audience online reactions at each of these three stages are depicted by ASIE with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of ASIE.

We study path auction mechanisms for buying path between two given nodes in a social network, where edges are owned by strategic agents. The well known VCG mechanism is the unique solution that guarantees both truthfulness and efficiency. However, in social network environments, the mechanism is vulnerable to false-name manipulations where agents can profit from placing multiple bids under fictitious names. Moreover, the VCG mechanism often leads to high overpayment. In this paper, we present core-selecting path mechanisms that are robust against false-name bids and address the overpayment problem. Specifically, we provide a new formulation for the core, which greatly reduces the number of core constraints. Based on the new formulation, we present a Vickery-nearest pricing rule, which finds the core payment profile that minimizes the L∞distance to the VCG payment profile. We prove that the Vickery-nearest core payments can be computed in polynomial time by solving linear programs. Our experiment results on real network datasets and reported cost dataset show that our Vickery-nearest core-selecting path mechanism can reduce VCG's overpayment by about 20%.

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.

The analysis of communities and their evolution in dynamic networks is a challenging research with broad applications. The recent studies have found that the overlaps between communities are more densely connected than the non-overlapping parts in some real networks. The findings are different from the present concepts of the overlapping communities. Existing methods may fail to detect this kind of communities. In this paper, we first extend the findings to analyze dynamic networks and develop an effective algorithm for detecting dense overlapping communities and their evolution in a unified process by using evolutionary clustering. We also introduce genetic algorithm with multidimensional chromosome to describe nodes belonging to multiple communities in our framework. The experimental studies demonstrate that our method successfully captures dense overlaps and identifies relevant communities more accurately than the state-of-the-art methods in dynamic networks.

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi-label dataset. Classifier Chains(CC) is a popular multi-label classification algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi-label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi-label dataset. Classifier Chains(CC) is a popular multi-label classified algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi-label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried out for years so as to detect anomalies as soon as possible. Among them, machine learning is one of the most used techniques. Previous work tries to improve detection by choosing various classifiers, which has achieved some success. But few has considered the different losses each anomaly might cause. As we know, anomalies with higher significance will cause higher losses. In this paper, we aim to minimize the losses by proposing an improved cost-sensitive GBDT algorithm named LF-GBDT. LF-GBDT is designed to optimize a self-defined loss function. Experiments with both traditional classification algorithms such as CART, Adaboost etc. And cost-sensitive algorithms such as MetaCost, CSC show that our method can both improve the detection of important anomalies and reduce the total losses.

Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.

Model checking, which can be represent as the paradigm "specifying→verifying", is an effective technology for automatic system verification. But model checking is generally used only to verify the correctness of a system, not to modify it. Sometimes this can be a major limitation. We propose a framework for ATL alpha beta model revision in this paper, study the computational complexity of its related problems, and show that the proposed framework consists with the minimal change principle in propositional belief update. By this paper, We extend the paradigm of ATL model checking to "specifying→verifying→revising" and establish the theoretical basis for automatic revising the strategic ability related properties of coordinated open systems.

The booming development of Online Social Networks (OSNs) provides a novel way of expressing emotions. Research on emotion analysis for tweets in OSNs is becoming increasingly popular in recent years. Traditional emotion analysis only classifies one tweet into a single emotion category. However, in reality, a tweet may belong to several different emotion categories. In the paper, our goal is to predict all emotion labels of each tweet. We use graphic emoticons, punctuation expressions together with a tiny but accurate lexicon to label data and provide a Multi-label Emotion Classification algorithm (MEC) for tweets in Weibo (so called Chinese Twitter). Our method has superior performance to the state-of-the-art method under both single-label and multi-label evaluation measures. We also carried out a case study on Weibo dataset of Malaysia Missing Flight. We came to several meaningful conclusions such as "The outbreak of Anger has a delay after breaking point of Sadness".

Text summarization is of great importance to solve information overload. Salience and coverage are two most important issues for summaries. Most existing models extract summaries by selecting the top sentences with highest scores without using the relationships between sentences, and usually represent the sentences simply basing on lexical or statistical features. As a result, those models can not achieve salience or coverage very well. In this paper, we propose a novel summarization model called Sentence Selection with Semantic Representation (SSSR). SSSR ensures both salience and coverage by learning semantic representations for sentences and applying a well-designed selection strategy to select summary sentences. The selection strategy used in SSSR is to select sentences that can reconstruct the original document with least distortion by means of linear combination. Besides, we improve our selection strategy by reducing redundant information. Then we learn two semantic representations for sentences: (1) weighted mean of word embeddings, (2) deep coding. Both of them are semantic and compact, and can capture similarities between sentences. Extensive experiments on datasets DUC2006 and DUC2007 validate our model.

Landmark heuristics are perhaps the most accurate current known admissible heuristics for optimal planning. A disjunctive action landmark can be seen a form of at-least-one constraint on the actions it contains. In many domains, some critical propositions have to be established for a number of times.Propositional landmarks are too weak to express this kind of constraints.In this paper, we propose to generalize landmarks to multi-valued landmarks to represent the more general cardinality constraints. We present a class of local multi-valued landmarks that can be efficiently extracted from propositional landmarks.By encoding multi-valued landmarks into CNF formulas, we can also use SAT solvers to systematically extract multi-valued landmarks.Experiment evaluations show that multi-valued landmark based heuristics are more close to $h^*$ andcompete favorably with the state-of-the-art of admissible landmark heuristics on benchmark domains.

Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.

Mining community structures has become a general problem which exists in many fields including: Computer-Science, Mathematics, Physics, Biology, Sociology and so on. It has developed rapidly and been used widely in many applications: web data mining, social network analysis, criminal network mining, protein interaction network analysis, metabolic network analysis, genetic network analysis, customers relationship mining and user online behavior analysis, etc. Most community detection algorithms try to obtain the global information of the network, but increasing large scale of the current network makes it computationally expensive. In the meanwhile, the different influence and different behavior of nodes in the network are ignored. In fact, if we know the local information of the network or the interested node, we can easily detect the local community. This paper proposes a multi-resolution local community detection algorithm named MRCDA which uses local structural information in the network to optimize the multi-resolution modularity based on the Potts spin-glass model. A local community can be detected through continuous optimization of the function by expanding from an initial influential node computed by a modified PageRank sorting algorithm. The proposed MRCDA has been tested on both synthetic and real world networks and tested against other algorithms. The experiments demonstrate its efficiency and accuracy.

As more and more people join social network, viral marketing on online social network becomes a new trend of advertising. Motivated by this, plenty of research focuseson how to maximize the information propagation, which is called the influence maximization problem. Traditional work has made significant progress on this topic. However all ad companies have marketing budget, the research of influence maximization problem should take account of cost control. Under the condition of cost control, we model each user's cost of helping spread information as a feature of each node in the network. Then we modify several most widely studied algorithms to suit the new model. In this paper, a new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes. Further improvements, based on strategy of partial node loading and submodular property of spread function, make CPP-SNS more effective in practical scenarios. Extensive experiments show this method has a good performance in different social networks. Based on results of our research, we also provide some advice for the practical marketing.

Most community detection algorithms are trying to obtain the global information of the network. But increasingly large scale of the current network makes accessing to global information very difficult. In the meanwhile, the network shows power-law distribution and sparse features. And local community mining algorithms which use these features have more advantages over global mining methods. In this paper, we proposed a local community detection algorithm based on the core members named LLCDA (Leader based Local Community Detecting Algorithm) which uses local structural information in the network to optimize a local objective function. A local community can be detected through continuous optimization of the function by expanding from an initial core member computed by a modified PageRank sorting algorithm. The proposed LLCDA algorithm has been tested on both synthetic and real world networks, and it has been compared with other community detecting algorithms. The experimental results validated our proposed LLCDA and showed that significant improvements have been achieved by this technique.

The issues of coalition formation have been investigated from many aspects and recently more and more attention has been paid to overlapping coalition formation. The (optimal) coalition structure generation(CSG) problem is one of the essential problems in coalition formation which is an important topic of cooperation in multiagent system. In this paper, we strictly define the coalition structure generation problem of overlapping extensions. Based on these definitions, we systematically prove some computational complexity results for overlapping coalition formation(OCF) games and threshold task games(TTGs). Moreover, dynamic programming and greedy approaches are adopted to solve the CSG for TTG.

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method achieves state-of-the-art performance in the image denoising task. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning.

The study of community detection has received more and more attention in recent years, the problem is very difficult and of great importance in many fields such as sociology, biology and computer science. But most of the algorithms proposed so far could not utilize the weight information within weighted networks, and many of them are so time-consuming that they are not fit for the large-scale networks. We propose a new center-based method, which is especially designed for weighted networks. And the method is also suitable for large-scale network because of its low computational complexity. We demonstrate our method on a synthetic network and two real-world networks. The result shows the high efficiency and precision of our method.

Detecting communities from networks has been given great attention these years. The traditional approaches were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. We proposed a novel algorithm LBLC (local based link community) to detect link communities in the network based on local information. A local link community can be detected by maximizing a local link fitness function from a seed link, which was ranked by another algorithm previously. The proposed LBLC algorithm has been tested on both synthetic and real world networks, and it has been compared with other link community detecting algorithm. The experimental results showed LBLC achieves significant improvement on link community structure.

Strategic ability updating relates to establishing some required properties, which can be expressed by strategic abilities, in a multicomponent reactive system. We model such a reactive system as a concurrent game structure (CGS), which is the semantic model of Alternating-time Temporal Logic (ATL). Then, we propose coalitional commitment as a tool for achieving the required strategic ability updating. Intuitively, a coalitional commitment can extend the state space of a CGS by a context function and then delete some transitions by a coalitional normative system (CNS). We propose coordinated ATL (co-ATL) for reasoning about strategic abilities in the structures obtained from a CGS by implementing a CNS. The model-checking problem for co-ATL is proved to be PTIME-complete, just like that of ATL, and is thus tractable. Then, we characterize the limitation of coalitional commitment power by identifying the set of co-ATL formulas whose satisfaction cannot be established and the set of co-ATL formulas whose satisfaction cannot be avoided. Afterward, we show that the effectiveness problem, feasibility problem, and synthesis problem for coalitional commitment are PTIME-complete, NP-complete, and FNP-complete, respectively. Finally, we treat the coalitional commitment synthesis problem as an extended planning problem and present an algorithm based on the planning as model checking paradigm. Our work can be seen as an improvement for both social law research and planning via model checking research.

Based on the planning via model checking paradigm, we address the problem of coalitional planning in this paper. Informally, coalitional planning is the problem of planning for a subset of agents in a multi-agent system to force the whole multi-agent system to satisfy some goals. We use the language of ATL as the goal language and the semantic structure of ATL, i.e., concurrent game structure, to formalize the planning domain. We separate the concept of goal and planning object and use execution structures to interpret the goals. And then, we define a algorithm for coalitional planning and formally prove its correctness. Distinguished from the previous work, in coalitional planning all the ATL formulas can be considered as goals, thus the expressive power of ATL is sufficiently applied.