Showing items 1 - 16 of 16

A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards

- Wang, Wei, Chen, Shang-Liang, Chen, Liang-Bi, Chang, Wan-Jung

  • Authors: Wang, Wei , Chen, Shang-Liang , Chen, Liang-Bi , Chang, Wan-Jung
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 5, no. (2017), p. 10817-10833
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  • Description: In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.

Application of SVM in citation information extraction

- Liang, Jiguang, Layton, Robert, Wang, Wei

  • Authors: Liang, Jiguang , Layton, Robert , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Support Vector Machines are an effective form of binary-class classification algorithm. To enhance the utilization of text structural features for information extraction, which are greatly restricted by the Hidden Markov Model (HMM), this paper proposes a support vector machine multi-class classification based on Markov properties to extract the information from a citation database. The proposed model extracts symbol characteristics as features and composes a binary tree of the transition probabilities. Experiments show that the proposed method outperforms HMM and basic SVM methods. © 2011 IEEE.

Attributed collaboration network embedding for academic relationship mining

- Wang, Wei, Liu, Jiaying, Tang, Tao, Tuarob, Suppawong, Xia, Feng, Gong, Zhiguo, King, Irwin

  • Authors: Wang, Wei , Liu, Jiaying , Tang, Tao , Tuarob, Suppawong , Xia, Feng , Gong, Zhiguo , King, Irwin
  • Date: 2021
  • Type: Text , Journal article
  • Relation: ACM Transactions on the Web Vol. 15, no. 1 (2021), p.
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  • Description: Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding. © 2020 ACM.

Collaborative filtering with network representation learning for citation recommendation

- Wang, Wei, Tang, Tao, Xia, Feng, Gong, Zhiguo, Chen, Zhikui, Liu, Huan

  • Authors: Wang, Wei , Tang, Tao , Xia, Feng , Gong, Zhiguo , Chen, Zhikui , Liu, Huan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Big Data Vol. 8, no. 5 (2022), p. 1233-1246
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  • Description: Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is challenging due to the cold start problem and the lack of paper ratings. To address these challenges, in this article, we propose a collaborative filtering with network representation learning framework for citation recommendation, namely CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and MRR (Mean Reciprocal Rank). Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines. © 2015 IEEE.

Data-driven computational social science : A survey

- Zhang, Jun, Wang, Wei, Xia, Feng, Lin, Yu-Ru, Tong, Hanghang

  • Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
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  • Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.

Determining the influence of visual training on EEG activity patterns using association rule mining

- Yan, Fangang, Watters, Paul, Wang, Wei

  • Authors: Yan, Fangang , Watters, Paul , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: To confirm that visual training can change EEG patterns by association rule mining method, firstly, we collected the EEG of people who are under a long-term visual professional training (visual training group) and novice people (control group) during a specific mental tasks. Secondly, we determined the difference of brain electrical activity between the two groups using machine learning methods. Thirdly, we discovered distinct patterns using association rule algorithm, finding that the two groups were separable based on their completion of visual professional cognitive tasks. In the beta band, visual training group showed a specific and significant association pattern which included FP1 and C4. The results indicate that the EEG patterns were modified because of visual professional training. We further discuss the impact of long-term visual professional training on the EEG. © 2011 IEEE.

Early-stage reciprocity in sustainable scientific collaboration

- Wang, Wei, Ren, Jing, Alrashoud, Mubarak, Xia, Feng, Mao, Mengyi, Tolba, Amr

  • Authors: Wang, Wei , Ren, Jing , Alrashoud, Mubarak , Xia, Feng , Mao, Mengyi , Tolba, Amr
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Informetrics Vol. 14, no. 3 (2020), p.
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  • Description: Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency. © 2020 Elsevier Ltd.
  • Description: The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-78. This work is partially supported by China Postdoctoral Science Foundation ( 2019M651115 ).

Is scientific collaboration sustainability predictable?

- Wang, Wei, Cui, Zixin, Gao, Tong, Yu, Shuo, Kong, Xiangjie, Xia, Feng

K-AP clustering algorithm for large scale dataset

- Liu, Chao, Hay, Rosemary, Wang, Wei

  • Authors: Liu, Chao , Hay, Rosemary , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: Affinity propagation clustering algorithm is with a broad value in science and engineering because of it no need to input the number of clusters in advances, robustness and good generalization. But the algorithm needs the initial similarity (the distance between any two points) as a parameter, a lot of time and storage space is required for the calculation of similarity. It's limited to apply to cluster of the large amounts of data. To solve problem, this paper brings forward K-AP cluster algorithm which integrate k-means algorithm to AP algorithm to decrease time-consuming and space superiority. The results show the K-AP algorithm is faster than the original algorithm processing in speed, and it can cluster large amounts of data, and achieve better results. © 2011 IEEE.

Not every couple is a pair : a supervised approach for lifetime collaborator identification

- Wang, Wei, Wan, Liangtian, Kong, Xiangjie, Gong, Zhiguo, Xia, Feng

  • Authors: Wang, Wei , Wan, Liangtian , Kong, Xiangjie , Gong, Zhiguo , Xia, Feng
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019, Xian, 8-12 July 2019
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  • Description: While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar's early profile. For this purpose, we propose a supervised approach by leveraging scholars' local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration. © Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.

Parameter optimization for Support Vector Machine Classifier with IO-GA

- Zhou, Jing, Maruatona, Omaru, Wang, Wei

  • Authors: Zhou, Jing , Maruatona, Omaru , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: The Support Vector Machine method has a good learning and generalization ability. Unfortunately, there are no comprehensive theories to guide the parameter selection of the SVM, which largely limits its application. In order to get the optimal parameters automatically, researchers have tried a variety of methods. Using genetic algorithms to optimize parameters of an SVM Classifier has become one of the favorite methods in recent years. In this paper, we explain how the Standard Genetic Algorithm (SGA) causes the problem of premature convergence and limits the accuracy of the SVM. We also put forward a new genetic algorithm with improved genetic operators (IO-GA) to optimize the SVM classifier's parameters. Experimental results show that the parameters obtained by this method can greatly improve the classification performance of SVM. We therefore conclude that this method is effective. © 2011 IEEE.

Scholar2vec : vector representation of scholars for lifetime collaborator prediction

- Wang, Wei, Xia, Feng, Wu, Jian, Gong, Zhiguo, Tong, Hanghang, Davison, Brian

  • Authors: Wang, Wei , Xia, Feng , Wu, Jian , Gong, Zhiguo , Tong, Hanghang , Davison, Brian
  • Date: 2021
  • Type: Text , Journal article
  • Relation: ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
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  • Description: While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.

The evolution of Turing Award Collaboration Network : bibliometric-level and network-level metrics

- Kong, Xiangjie, Shi, Yajie, Wang, Wei, Ma, Kai, Wan, Liangtian, Xia, Feng

  • Authors: Kong, Xiangjie , Shi, Yajie , Wang, Wei , Ma, Kai , Wan, Liangtian , Xia, Feng
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 6, no. 6 (2019), p. 1318-1328
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  • Description: The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades. © 2014 IEEE.

Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles

- Wang, Wei, Xia, Feng, Nie, Hansong, Chen, Zhikui, Gong, Zhiguo

  • Authors: Wang, Wei , Xia, Feng , Nie, Hansong , Chen, Zhikui , Gong, Zhiguo
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
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  • Description: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**

Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data

- Wang, Wei, Gong, Zhiguo, Ren, Jing, Xia, Feng, Lv, Zhihan, Wei, Wei

  • Authors: Wang, Wei , Gong, Zhiguo , Ren, Jing , Xia, Feng , Lv, Zhihan , Wei, Wei
  • Date: 2021
  • Type: Text , Journal article
  • Relation: ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
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  • Description: Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.

Video games' educational evaluation model based on BP neural network

- Wang, Wei, Maruatona, Omaru, Qian, Huang

  • Authors: Wang, Wei , Maruatona, Omaru , Qian, Huang
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Evaluating the education value of video games is a complex task. Based on the educational evaluation system for video games, we establish a corresponding automated evaluation model using the Back-propagation (BP) neural network technology. After training with the expert knowledge, the model not only has the experience of experts, but also has the computational capacity to evaluate new cases. © 2011 IEEE.
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