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Etymo: A New Discovery Engine for AI Research [article]

Weijian Zhang, Jonathan Deakin, Nicholas J. Higham, Shuaiqiang Wang
2018 arXiv   pre-print
We present Etymo (, a discovery engine to facilitate artificial intelligence (AI) research and development. It aims to help readers navigate a large number of AI-related papers published every week by using a novel form of search that finds relevant papers and displays related papers in a graphical interface. Etymo constructs and maintains an adaptive similarity-based network of research papers as an all-purpose knowledge graph for ranking, recommendation, and visualisation.
more » ... d visualisation. The network is constantly evolving and can learn from user feedback to adjust itself.
arXiv:1801.08573v1 fatcat:umjnuguhlvdxri5borwn5rjvjm


Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma
2014 ACM Transactions on Intelligent Systems and Technology  
Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating-based and ranking-based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study,
more » ... y. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.
doi:10.1145/2542048 fatcat:b3qynid4ivdcpmf5z5xjlylxqa

Linear feature extraction for ranking

Gaurav Pandey, Zhaochun Ren, Shuaiqiang Wang, Jari Veijalainen, Maarten de Rijke
2018 Information retrieval (Boston)  
We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors
more » ... ocument vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generated new matrix can match the learning to rank problem. Extensive experiments on benchmark datasets show the performance gains of LifeRank in comparison with state-of-the-art feature selection algorithms.
doi:10.1007/s10791-018-9330-5 fatcat:y5z5vewspvgufilfzv6xrzc5ia

Evolving choice structures for genetic programming

Shuaiqiang Wang, Jun Ma, Jiming Liu, Xiaofei Niu
2010 Information Processing Letters  
It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some "if-structures" functions according to their problems by combining "if-else" statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination
more » ... d of termination criterion in the GP process named "Combination Termination Criterion" (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.
doi:10.1016/j.ipl.2010.07.014 fatcat:27j2qo3d3ncefnq5lhscf37dpe

Polygene-based evolution

Shuaiqiang Wang, Byron J. Gao, Shuangling Wang, Guibao Cao, Yilong Yin
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolution process. In traditional EAs, the primitive evolution unit is gene, where genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality
more » ... olving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: polygene discovery, polygene planting, and polygene-compatible evolution. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in accuracy and efficiency improvement.
doi:10.1145/2396761.2398616 dblp:conf/cikm/WangGWCY12 fatcat:4bzeusimtzf3lci5gjisgagkza

Formal behavior modeling and effective automatic refinement

Shuaiqiang Wang, Jun Ma, Qiang He, Jiancheng Wan
2010 Information Sciences  
Wang et al. / Information Sciences 180 (2010) 3894-3913 The parameter '' * " in the predicate PðÃ; h O Þ means any type of the variables.  ... 
doi:10.1016/j.ins.2010.06.024 fatcat:ilgbsa3a6vgz5lk4zgpzus4bsy

Learning to Detect Web Spam by Genetic Programming [chapter]

Xiaofei Niu, Jun Ma, Qiang He, Shuaiqiang Wang, Dongmei Zhang
2010 Lecture Notes in Computer Science  
Web spam techniques enable some web pages or sites to achieve undeserved relevance and importance. They can seriously deteriorate search engine ranking results. Combating web spam has become one of the top challenges for web search. This paper proposes to learn a discriminating function to detect web spam by genetic programming. The evolution computation uses multi-populations composed of some small-scale individuals and combines the selected best individuals in every population to gain a
more » ... ion to gain a possible best discriminating function. The experiments on WEBSPAM-UK2006 show that the approach can improve spam classification recall performance by 26%, F-measure performance by 11%, and accuracy performance by 4% compared with SVM.
doi:10.1007/978-3-642-14246-8_5 fatcat:ntno4olt6ra45h55xgtha6p7sa

Singular value decomposition based minutiae matching method for finger vein recognition

Fei Liu, Gongping Yang, Yilong Yin, Shuaiqiang Wang
2014 Neurocomputing  
Recently, finger vein recognition has received considerable attention in the biometric recognition field. Originating from fingerprint recognition, minutiae-based methods are recognized as an important branch, which attempts to discover minutia patterns from finger vein images for matching and recognition. However, the accuracy of these methods is generally unsatisfactory. One of the most challenging problems is that, the correspondences of two minutia sets are difficult to obtain resulting
more » ... btain resulting from the rotation, translation and deformation of the finger vein images. Another critical problem is that, the current available feature descriptors for minutia representation are weak and insufficient. In this paper, we propose SVDMM, a singular value decomposition (SVD)-based minutiae matching method for finger vein recognition, which involves three stages: (I) minutia pairing, (II) false removing and (III) score calculating. In particular, stage I discovers minutia pairs via SVD-based decomposition of the correlation-weighted proximity matrix. Stage II removes false pairs based on the local extensive binary pattern (LEBP) for increasing the reliability of the correspondences. Stage III determines the matching score of the input and template images by the 'average' matching degree of all their precise minutia pairs. Extensive experiments demonstrate that our work not only performs better than the similar works in the literature, but also has great potential to achieve comparable performance to other categories of state-of-the-art methods.
doi:10.1016/j.neucom.2014.05.069 fatcat:7mq4c3h23jfmpc32yh642piuoy

Importance weighted passive learning

Shuaiqiang Wang, Xiaoming Xi, Yilong Yin
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Importance weighted active learning (IWAL) introduces a weighting scheme to measure the importance of each instance for correcting the sampling bias of the probability distributions between training and test datasets. However, the weighting scheme of IWAL involves the distribution of the test data, which can be straightforwardly estimated in active learning by interactively querying users for labels of selected test instances, but difficult for conventional learning where there are no
more » ... re are no interactions with users, referred as passive learning. In this paper, we investigate the insufficient sampling bias problem, i.e., bias occurs only because of insufficient samples, but the sampling process is unbiased. In doing this, we present two assumptions on the sampling bias, based on which we propose a practical weighting scheme for the empirical loss function in conventional passive learning, and present IWPL, an importance weighted passive learning framework. Furthermore, we provide IWSVM, an importance weighted SVM for validation. Extensive experiments demonstrate significant advantages of IWSVM on benchmarks and synthetic datasets.
doi:10.1145/2396761.2398611 dblp:conf/cikm/WangXY12 fatcat:eivbut6xwzb6lb6m3s637a34du

User-Centric Organization of Search Results

Byron J. Gao, David Buttler, David C. Anastasiu, Shuaiqiang Wang, Peng Zhang, Joey Jan
2013 IEEE Internet Computing  
Wang is an associate professor at Shandong University of Finance and Economics. His research interests include information retrieval, data mining, and machine learning.  ...  User effort Ω (points) R 1 R 5 R 10 RL : Ranked list IC : Initial clustering AC : Aggregated clustering PC : Personalized clustering Shuaiqiang Peng Joey Mobile S E P T E M B E  ... 
doi:10.1109/mic.2013.57 fatcat:vzv6dhuuvbarlj2kcycrclbumy

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

Denis Kotkov, Jari Veijalainen, Shuaiqiang Wang
2018 Computing  
doi:10.1007/s00607-018-0687-5 fatcat:6fiohjom3renncuxrucfzpgvw4

Mining and ranking users' intents behind queries

Pengjie Ren, Zhumin Chen, Jun Ma, Shuaiqiang Wang, Zhiwei Zhang, Zhaochun Ren
2015 Information retrieval (Boston)  
Wang et al. ranked queries' sub-intents by optimizing both their relevance and diversity.  ...  Wang and Zhai first learned a given query's aspects from users' query logs with the star clustering algorithm.  ... 
doi:10.1007/s10791-015-9271-1 fatcat:px3sb2gvizfgdodakgsstszgui

Distributed collaborative filtering with singular ratings for large scale recommendation

Ruzhi Xu, Shuaiqiang Wang, Xuwei Zheng, Yinong Chen
2014 Journal of Systems and Software  
., 2001; Wang et al., 2012) and model-based (Liu et al., 2009; Rendle et al., 2009; Shani et al., 2005; Si and Jin, 2003; Sun et al., 2012; Weimer et al., 2007) .  ... 
doi:10.1016/j.jss.2014.04.045 fatcat:lqybzskph5dwrgdwoww6qnsydu

A Cooperative Coevolution Framework for Parallel Learning to Rank

Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
2015 IEEE Transactions on Knowledge and Data Engineering  
Wang et al. [23] proposed RankIP, a ranking function discovery approach based on immune programming [25] . Other algorithms are surveyed in [37] .  ...  Wang et al. [23] demonstrates that the performance of RankIP is robust to s e . Equation (6) is a convex function that assigns higher affinity scores for antibodies than linear scale-up.  ... 
doi:10.1109/tkde.2015.2453952 fatcat:gltmjpyaencx5dnulatxppwyyy


Mohamed Abdel Maksoud, Gaurav Pandey, Shuaiqiang Wang
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. Generally in search engines, utilization of semantics between words is favorable for performance improvement. Even though ambiguous query words have multiple semantic meanings, search engines can return diversified results to satisfy different users' information needs. But for CitySearcher, mismatched semantic relationships can lead to extremely unsatisfactory results. For example, the
more » ... or example, the city Sale would incorrectly rank high for the interest shopping because of semantic interpretations of the words. Thus in our system, the main challenge is to eliminate the mismatched semantic relationships resulting from the side effect of the semantic models. In the previous case, we aim to ignore the semantics of a city's name which is not indicative of the city's characteristics. In CitySearcher, we use word2vec, a very popular word embedding technique to estimate the semantics of the words and create the initial ranks of the cities. To reduce the effect of the mismatched semantic relationships, we generate a set of features for learning based on a novel clustering-based method. With the generated features, we then utilize learning to rank algorithms to rerank the cities for return. We use the English version of Wikivoyage dataset for evaluation of our system, where we sample a very small dataset for training. Experimental results demonstrate the performance gain of our system over various standard retrieval techniques.
doi:10.1145/3077136.3080742 dblp:conf/sigir/MaksoudPW17 fatcat:h4w2ynzbubfw7enr66s2zew5ym
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