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Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In this study, we developed a deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A novel convolutional neural network, Residual Whole Map Attention Network (RWMAN) was developed as the generator network for our MedSRGAN in extracting the useful information through different channels, as well as paying more attention on meaningful regions. In addition, a weighted sum of content loss, adversarial loss, and adversarial feature loss were fused to form a multi-task loss function during the MedSRGAN training. 242 thoracic CT scans and 110 brain MRI scans were collected for training and evaluation of MedSRGAN. The results showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. A mean opinion score (MOS) test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.

Relational fact extraction aims to extract semantic triplets from unstructured text. In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective. An efficient model, aDjacency lIst oRiented rElational faCT (DIRECT), is proposed based on this analytical framework. To alleviate challenges of error propagation and sub-task loss equilibrium, DIRECT employs a novel adaptive multi-task learning strategy with dynamic sub-task loss balancing. Extensive experiments are conducted on two benchmark datasets, and results prove that the proposed model outperforms a series of state-of-the-art (SoTA) models for relational triplet extraction., 13 pages, 3 figures, accepted by findings of ACL 2021

Nowadays, with the quick availability and growth of formulae on the Web, the question of how to effectively retrieve the relevant documents about formulae, namely formula retrieval, has attracted much attention from the researchers of mathematical information retrieval (MIR). Existing MIR search engines have explored much information of formulae such as characters, layout structure, the formula context. However, little attention has been paid to the link or citation relations of formulae among different documents, while these relations are helpful for searching some related formulae whose appearances are not similar to the query formula. Therefore, in this paper, we design a Formula Citation Graph (FCG) to ‘dig out’ the link or citation relations between formulae. FCG has two main advantages: 1) The graph could generate the descriptive keywords of formulae to enrich the semantics of formula queries. 2) The graph is employed to balance the ranking results between the text and structure matching. The experimental results demonstrate that the link or citation relations among formulae are helpful for MIR.

Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales., 10 pages, 5 figures

Detecting dark businesses in a decentralized eCommerce ecosystem (e.g. eBay, eBid, and Taobao) is a critical research problem. In this paper, we investigate the characteristics of dark implicit products, the associated buyer seeking behaviors, and features of classification model. Results demonstrate that dark implicit product detection is a challenging problem, while buyer seeking behavior information could be useful as a critical alternative to address this problem.

Technological advancements have led to increasing availability of erotic literature and pornography novels online, which can be alluring to adolescence and children. Unfortunately, because of the inherent complexity of these indecent contents and training data sparseness, it is a challenging task to detect these readings in the Cyberspace while children can easily access them. In this study, we propose a novel framework, Joint Learning of Content and Human Attention (GoodMan), to identify indecent readings by augmenting natural language understanding models with large scale human reading behaviors (dwell time per page) on portable devices. From the text modeling viewpoint, the innovative joint attention trained by joint learning is employed to orchestrate the content attention and human behavior attention via the BiGRU. From the data augmentation perspective, various users' reading behaviors on the same text can generate considerable training instances with joint attention, which can be effective to address the cold start problem. We conduct an extensive set of experiments on an online ebook dataset (with human reading behaviors on portable devices). The experimental results show insights into the task and demonstrate the superiority of the proposed model against alternative solutions.

Implied semantics is a complex language act that can appear everywhere on the Cyberspace. The prevalence of implied spam texts, such as implied pornography, sarcasm, and abuse hidden within the novel, tweet, microblog, or review, can be extremely harmful to the physical and mental health of teenagers. The non-literal interpretation of the implied text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspired by human reading comprehension, we propose a novel, simple, and effective deep neural framework, called Skim and Intensive Reading Model (SIRM), for figuring out implied textual meaning. The proposed SIRM consists of three main components, namely the skim reading component, intensive reading component, and adversarial training component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both sentence-level and paragraph-level representation, which encapsulates the current (local) embedding and the contextual information (context) with a dense connection. More specifically, the contextual information includes the near-neighbor information and the skim information mentioned above. Finally, besides the common training loss function, we employ an adversarial loss function as a penalty over the skim reading component to eliminate noisy information (noise) arisen from special figurative words in the training data. To verify the effectiveness, robustness, and efficiency of the proposed architecture, we conduct extensive comparative experiments on an industrial novel dataset involving implied pornography and three sarcasm benchmarks. Experimental results indicate that (1) the proposed model, which benefits from context and local modeling and consideration of figurative language (noise), outperforms existing state-of-the-art solutions, with comparable parameter scale and running speed; (2) the SIRM yields superior robustness in terms of parameter size sensitivity; (3) compared with ablation and addition variants of the SIRM, the final framework is efficient enough.

The recent success of deep graph embedding innovates the graphical information characterization methodologies. However, in real-world applications, such a method still struggles with the challenges of heterogeneity, scalability, and multiplex. To address these challenges, in this study, we propose a novel solution, Genetic hEterogeneous gRaph eMbedding (GERM), which enables flexible and efficient task-driven vertex embedding in a complex heterogeneous graph. Unlike prior efforts for this track of studies, we employ a task-oriented genetic activation strategy to efficiently generate the “Edge Type Activated Vector” (ETAV) over the edge types in the graph. The generated ETAV can not only reduce the incompatible noise and navigate the heterogeneous graph random walk at the graph-schema level, but also activate an optimized subgraph for efficient representation learning. By revealing the correlation between the graph structure and task information, the model interpretability can be enhanced as well. Meanwhile, an activated heterogeneous skip-gram framework is proposed to encapsulate both topological and task-specific information of a given heterogeneous graph. Through extensive experiments on both scholarly and e-commerce datasets, we demonstrate the efficacy and scalability of the proposed methods via various search/recommendation tasks. GERM can significantly reduces the running time and remove expert-intervention without sacrificing the performance (or even modestly improve) by comparing with baselines.

We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. A “self-diversity” criterion is proposed for measuring the “worthiness” of a candidate for annotation. A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation. The preliminary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the performance of a series of conventional active learning models for Chinese spam detection task. To the best of our knowledge, this is the first work to integrate active learning and semi-supervised generative learning for text spam detection.

Aspect category detection is an essential task for sentiment analysis and opinion mining. However, the cost of categorical data labeling, e.g., label the review aspect information for a large number of product domains, can be inevitable but unaffordable. In this study, we propose a novel problem, cross-domain aspect category transfer and detection, which faces three challenges: various feature spaces, different data distributions, and diverse output spaces. To address these problems, we propose an innovative solution, Traceable Heterogeneous Graph Representation Learning (THGRL). Unlike prior text-based aspect detection works, THGRL explores latent domain aspect category connections via massive user behavior information on a heterogeneous graph. Moreover, an innovative latent variable "Walker Tracer" is introduced to characterize the global semantic/aspect dependencies and capture the informative vertexes on the random walk paths. By using THGRL, we project different domains' feature spaces into a common one, while allowing data distributions and output spaces stay differently. Experiment results show that the proposed method outperforms a series of state-of-the-art baseline models., Accepted as a full paper of The 28th ACM International Conference on Information and Knowledge Management (CIKM '19)

It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.

The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection., Comment: Accepted as a full paper of 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing

With the ever-increasing volume of formulae on the Web, formula retrieval has drawn much attention from researchers. However, most of the existing researches on formula retrieval treat each formula within an article equally, while different formulae in the same article have different importance to the article. In this paper, we address the issue to rank formulae within an article based on their importance. To evaluate the importance of each formula within an article, a formula citation graph is firstly built in a large scale corpus. And the inter-articles features of formulae are extracted by the link topology analysis of formulae based on the graph. Then the word embedding model is explored to extract the inner-article features by mining the semantic relevance between a formula and the corresponding article. Finally, we leverage learning to rank technique for formulae ranking within an article based on those features. The experimental results demonstrate that the proposed features are helpful for formula ranking and our approach yields better performance compared with other state-of-the-art methods.

While citation recommendation can be important for scholars, unfortunately, because of language barrier, some scientists cannot efficiently retrieve and consume the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Publication Content and Citation Representation Fusion (PCCRF). PCCRF can learn a representation function by mapping the publications, from various languages and repositories, to a low-dimensional joint embedding space from both content semantic and citation relation viewpoints. The proposed method can optimize the publication representations by maximizing the likelihood of observing network neighborhoods (which are generated by a semi-supervised random walk algorithm) of publications. Experimental results show that the proposed method can be promising for cross-language citation recommendation.

Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, ``mathematics content understanding'', for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings., Comment: The 27th ACM International Conference on Information and Knowledge Management (CIKM2018) 37--46

Metadata information extraction from academic papers is of great value to many applications such as scholar search, digital library, and so on. This task has attracted much attention from researchers in the past decades, and many templates-based or statistical machine learning (e.g. SVM, CRF, etc.)-based extraction methods have been proposed, while this task is still a challenge because of the variety and complexity of page layout. To address this challenge, we try introducing the deep learning networks to this task in this paper, since deep learning has shown great power in many areas like computer vision (CV) and natural language processing (NLP). Firstly, we employ the deep learning networks to model the image information and the text information of paper headers respectively, which allow our approach to perform metadata extraction with little information loss. Then we formulate the problem, metadata extraction from a paper header, as two typical tasks of different areas: object detection in the area of CV, and sequence labeling in the area of NLP. Finally, the two deep networks generated from the above two tasks are combined together to give extraction results. The primary experiments show that our approach achieves state-of-the-art performance on several open datasets. At the same time, this approach can process both image data and text data, and does not need to design any classification feature.

The citation relationships between publications, which are significant for assessing the importance of scholarly components within a network, have been used for various scientific applications. Missing citation metadata in scholarly databases, however, create problems for classical citation-based ranking algorithms and challenge the performance of citation-based retrieval systems. In this research, we utilize a two-step citation analysis method to investigate the importance of publications for which citation information is partially missing. First, we calculate the importance of the author and then use his importance to estimate the publication importance for some selected articles. To evaluate this method, we designed a simulation experiment-"random citation-missing"-to test the two-step citation analysis that we carried out with the Association for Computing Machinery ACM Digital Library DL. In this experiment, we simulated different scenarios in a large-scale scientific digital library, from high-quality citation data, to very poor quality data, The results show that a two-step citation analysis can effectively uncover the importance of publications in different situations. More importantly, we found that the optimized impact from the importance of an author first step is exponentially increased when the quality of citation decreases. The findings from this study can further enhance citation-based publication-ranking algorithms for real-world applications.

Citation metadata extraction plays an important role in academic information retrieval and knowledge management. Current works on this task generally use rule-based, template-based or learning-based approaches but these methods usually either rely on handcrafted features or are limited with domains. Recently, neural networks have shown strong ability in addressing sequence labeling tasks. In this paper, we propose a sequence labeling model for citation metadata extraction, called segment sequence labeling. Instead of inferring at word level, the input sequence is first divided into segments, and then features of the segments are computed to infer the label sequence of the segments. We first run experiments to validate the effectiveness of different parts of the model by comparing it with a CRF-based model and a neural network-based model. Experimental results show our model beats both models on most fields. Besides, our model is evaluated on public datasets UMass and Cora and has achieved significant performance improvement. Our model was trained on the data which were generated from BibTeX files collected on the Web and annotated automatically.

In practice, PDF files may be generated by different tools and their character information quality could be different. As a result, the approaches to detecting formulae from PDF documents usually have much different performance on different PDF files. To address this problem, in this paper we combine and refine the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model to detect formulae according to both their character and vision features. Based on the characteristic of PDF documents, we propose a series of strategies to train and optimize deep networks, such as the implicit class down-sampling strategy which can reduce the unbalancedness between formulae and other page elements (e.g., text paragraphs, tables, figures, etc.). The region proposal method is also redesigned to generate moderate formula candidates through combining the bottom-up and top-down layout analysis. The experimental results show that the combination of CNN and RNN can increase the robustness of our proposed detection method. Furthermore, the proposed method outperforms the existing formula detection methods on both a ground-truth dataset and a larger self-built dataset, which would be released and available for research purposes.

With more and more scientific documents becoming available in PDF format, recognition of formulae in these PDF documents is of great significance. In this paper, we propose a symbol dominance based formulae recognition approach to recovering formulae structures by using the rich information extracted directly from PDF files. The hierarchical structure of formula is represented by relationship tree, and the tree is built recursively based on symbol dominance, which considers both the spatial layout of symbols and the typesetting conventions of mathematics. In addition, we propose a special character recognition method to identify the formula characters with multiple components or variable unicode. Repeatable and comparable experiments have been done over two large datasets, IM2LATEX-100K and PDFME-10K. Experimental results demonstrate that our method is more adaptive and practical for PDF documents compared with other two existing available formulae recognition systems, INFTY and WYGIWYS.

This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are Abstract—Object detection in natural scenes has been widely researched in the past decade, and many deep learning based methods have achieved good performance on this task. This paper focuses on how to transfer and refine those object detection approaches from natural scene images to documents images, and proposes a deep learning-based page object (e.g., tables, formulae, figures) detection method. On the basis of traditional Convolutional Neural Network (CNN) based object detection methods, we redesign the region proposal method, the training strategy, the network structure and replace the Non-Maximum Suppression (NMS) with a dynamic programming algorithm. The experimental results show that it is essential to adjust some modules of the natural scene object detection approaches in order to better process the document images. The proposed method also achieved better performance compared with existing page object detection methods.

As data in many disciplines continues to grow at a frantic pace, a more heterogeneous set of information sources both can and need to be connected in innovative ways to improve information search and recommendation systems. Using information retrieval (IR) as an example, classical search techniques are based on limited information, including query terms, the content of documents, and basic user characteristics. However, recent studies have started to investigate additional information resources, including semantic web data, knowledge graphs, linguistic features, user email account content, social networks, user behavior, the search context, and other sources. Intuitively, a heterogeneous graph integrating all these information sources could enhance search performance. However, the complexity of such graphs challenge most existing graph mining algorithms. In this study, we propose a novel approach for a personalized random walk over a complex heterogeneous graph; which we refer to as Personalized Graph Navigation (PGN). Unlike earlier expert-guided random walk approaches, by using an EM framework, PGN estimates the personalized usefulness probability distribution for each edge type (the latent variable), which can be used as a random walk navigation profile for a user on a heterogeneous graph. While PGN can cope with information retrieval/recommendation problems at a low cost, this method also transforms a complex heterogeneous graph into a homogeneous graph, allowing existing homogeneous graph mining algorithms to be applied to a heterogeneous graph.

Helping students better understand scientific publications is one of the essential tasks of higher education. This paper presents a novel learning/reading environment that incorporates innovative scaffolding methods that aim to enable students to readily access open educational resources (OER) and read and comprehend the paper in a collaborative reading environment within a course. The new system, OER-based Collaborative PDF Reader (OCPR), captures and characterizes students’ emerging information needs when they read a paper, while also auto-recommending high quality OERs, e.g., presentation videos, slides, or Wikipedia pages, when students ask for clarifying content (OER-based Scaffolding). The study was conducted in a course for students to read an article, ask questions, and rate resources suggested by the OCPR system. The preliminary results show that the students tend to ask elementary questions and rate video resources higher than other resources. The results will inform future improvement of the OCPR system.

As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called "Chronological Citation Recommendation" which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final "Chronological Citation Recommendation" rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.

Scientific publication retrieval/recommendation has been investigated in the past decade. However, to the best of our knowledge, few efforts have been made to help junior scholars and graduate students to understand and consume the essence of those scientific readings. This paper proposes a novel learning/reading environment, OER-based Collaborative PDF Reader (OCPR), that incorporates innovative scaffolding methods that can: 1. auto-characterize student emerging information need while reading a paper; and 2. enable students to readily access open educational resources (OER) based on their information need. By using metasearch methods, we pre-indexed 1,112,718 OERs, including presentation videos, slides, algorithm source code, or Wikipedia pages, for 41,378 STEM publications. Based on the computational information need, we use text mining and heterogeneous graph mining algorithms to recommend high quality OERs to help students better understand the scientific content in the paper. Evaluation results and exit surveys for an information retrieval course show that the OCPR system alone with the recommended OERs can effectively assist graduate students better understand the complex STEM publications. For instance, 78.42% of participants believe the OCPR system and recommended OERs can provide precise and useful information they need, while 78.43% of them believe the recommended OERs are close to exactly what they need when reading the paper. From OER ranking viewpoint, MRR, MAP and NDCG results prove that learning to rank and cold start solutions can efficiently integrate different text and graph ranking features.

Scientific information recommendation is crucial to assist scholars for their researches. Citation recommendation is an important field of scientific recommendation. Traditional approaches ignore the chronological nature of the citation recommendation task. In this study, I propose the "Chronological Citation Recommendation," which assumes initial user information need could shift while they are looking for the papers in different time slices. Specifically, I employed a supervised dynamic topic model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that contains dynamic topic-based information, time-decay citation information and word-based information. I applied different meta-paths for different ranking hypotheses, which carried different types of information for citation recommendation in different time slices along with information need shifting. I plan to generate the final "Chronological Citation Recommendation" rankings by feature integration using Learning to Rank. "Chronological Citation Recommendation" will recommend time-series ranking lists based on initial user textual information need. Preliminary experiments on the ACM corpus show that chronological citation recommendation will significantly improve the citation recommendation performance.

Concepts have been used extensively in semantic annotating. Explicit Semantic Analysis (ESA) is a concept feature generator, which represents text by a concept-level vector, such as a vector of Wikipedia concepts. It is also considered a human-friendly way to annotate text - it generates concept vector that can be easily interpreted by human. We propose an approach, RescoredESA, based on ESA, according to aspects upon which ESA can enhance: 1) sometimes the output vectors do not assign high scores to concepts relevant to the text; 2) it considers words in the text when representing the text to concept-level vector while not considering the concepts explicitly occurring in the text, which can be an important source for assigning scores to ESA vector dimensions. We evaluate it against the 20 newsgroup classification task, and the result shows a slight enhancement when combining vectors from RescoredESA and bag-of-words.

With the development of academic research, the number of scientific papers has risen sharply, there is an urgent need to assist researchers in locating the candidate cited papers they are looking for. Classical relation-based and text-based approaches ignore the chronological nature of the citation recommendation task. In this study, we propose an innovative dynamic topic/citation influence model (DTCIM), which assumes user initial information need could shift while they are looking for the papers in different time slices. Specifically, we integrate text search and citation link navigation in a chronological dynamic topic model environment. Unlike previous studies, the new model characterizes user topical information need shifting and intrinsic citation time-decay. We apply this model for chronological citation recommendation, which can recommend time-series ranking lists based on users' initial textual information needs. Our experiment on the ACM sequential corpus shows that DTCIM is an effective model for chronological citation recommendation, comparing with classic models and algorithms.

Citation relationships between publications are important for assessing the importance of scholarly components (e.g., authors, publications, and venues) within a network. Missing citation metadata in scholarly databases, however, creates problems for classical citation-based ranking algorithms. In the ACM database, for example, 18.5% of publications don't have citation metadata. In this research we propose an innovative, 2-step method of citation analysis, to investigate the importance of publications for which citation data is missing. Preliminary evaluation results show that this method can effectively uncover the importance of publications without using citation metadata.

In addition to its broad popularity Wikipedia is also widely used for scholarly purposes. Many Wikipedia pages pertain to academic papers, scholars and topics providing a rich ecology for scholarly uses. Scholarly references and mentions on Wikipedia may thus shape the "societal impact" of a certain scholarly communication item, but it is not clear whether they shape actual "academic impact". In this paper we compare the impact of papers, scholars, and topics according to two different measures, namely scholarly citations and Wikipedia mentions. Our results show that academic and Wikipedia impact are positively correlated. Papers, authors, and topics that are mentioned on Wikipedia have higher academic impact than those are not mentioned. Our findings validate the hypothesis that Wikipedia can help assess the impact of scholarly publications and underpin relevance indicators for scholarly retrieval or recommendation systems.

Recently, SOA plays a more and more important role in software research and software development. On the basis of the research findings on SOA, this paper presents an innovative model for applying SOA: Multi-layer Structure For Dynamic Service Integration (MSFDSI). The paper expatiates on the organization of MSFDSI, on the basis of typical organization of SOA, MSFDSI adds a authorized institution and a service integration & analysis adapter to achieve the service authorization, service analysis and dynamic service integration. The paper also presents the hierarchy of MSFDSI, analyzes its service, proposes innovative concepts of coordination service and procedural service which traditional SOA models never contain, gives the Meta-model for service contracts and describes the main process of MSFDSI. Moreover, this paper gives a real case study of applying this model in a project of "Maritime and shipping integrated system of Yunnan Province". The case study shows that MSFDSI separates the business logic and the specific implementation technology, achieves the dynamic service integration successfully, improves the performance of system and provides a new and effective solution for applying SOA.