The significance and impact of winning an academic award : a study of early career academics
- Ren, Jing, Shi, Yajie, Shatte, Adrian, Kong, Xiangjie, Xia, Feng
- Authors: Ren, Jing , Shi, Yajie , Shatte, Adrian , Kong, Xiangjie , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022, Virtual, online, 20-24 June 2022, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
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- Description: Academic award plays an important role in an academic's careerparticularly for early career academics. Previous studies have primarilyfocused on the impact of awards conferred to academics whoe made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigatethe effect of awards conferred to academics at an earlier careerstage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluatethe change of a recipient's academic behavior after winningan academic award. Next, we propose a model to compare awardrecipients with academics who have similar performance beforewinning an academic award. In summary, we analyze the impact ofan award on the recipients' academic impact and their teams fromdifferent perspectives. Experimental results show that most recipientsdo have improvements in both productivity and citations afterwinning an academic award, while there is no significant impacton publication quality. In addition, receipt of an academic awardnot only expands recipients' collaboration network, but also has apositive effect on their team size. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Cross network representation matching with outliers
- Hou, Mingliang, Ren, Jing, Febrinanto, Febrinanto, Shehzad, Ahsan, Xia, Feng
- Authors: Hou, Mingliang , Ren, Jing , Febrinanto, Febrinanto , Shehzad, Ahsan , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, online, 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 951-958
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- Description: Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE.
- Authors: Hou, Mingliang , Ren, Jing , Febrinanto, Febrinanto , Shehzad, Ahsan , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, online, 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 951-958
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- Description: Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE.
Deep video anomaly detection : opportunities and challenges
- Ren, Jing, Xia, Feng, Liu, Yemeng, Lee, Ivan
- Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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- Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.
- Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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- Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.
Heterogeneous graph learning for explainable recommendation over academic networks
- Chen, Xiangtai, Tang, Tao, Ren, Jing, Lee, Ivan, Chen, Honglong, Xia, Feng
- Authors: Chen, Xiangtai , Tang, Tao , Ren, Jing , Lee, Ivan , Chen, Honglong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021, Virtual, Online, 14-17 December 2021, ACM International Conference Proceeding Series p. 29-36
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- Description: With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach. © 2021 ACM.
- Authors: Chen, Xiangtai , Tang, Tao , Ren, Jing , Lee, Ivan , Chen, Honglong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021, Virtual, Online, 14-17 December 2021, ACM International Conference Proceeding Series p. 29-36
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- Description: With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach. © 2021 ACM.
On the correlation between research complexity and academic competitiveness
- Ren, Jing, Lee, Ivan, Wang, Lei, Chen, Xiangtai, Xia, Feng
- Authors: Ren, Jing , Lee, Ivan , Wang, Lei , Chen, Xiangtai , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Kyoto, Japan, 30 November to 1 December 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12504 LNCS, p. 416-422
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- Description: Academic capacity is a common way to reflect the educational level of a country or district. The aim of this study is to explore the difference between the scientific research level of institutions and countries. By proposing an indicator named Citation-weighted Research Complexity Index (CRCI), we profile the academic capacity of universities and countries with respect to research complexity. The relationships between CRCI of universities and other relevant academic evaluation indicators are examined. To explore the correlation between academic capacity and economic level, the relationship between research complexity and GDP per capita is analysed. With experiments on the Microsoft Academic Graph data set, we investigate publications across 183 countries and universities from the Academic Ranking of World Universities in 19 research fields. Experimental results reveal that universities with higher research complexity have higher fitness. In addition, for developed countries, the development of economics has a positive correlation with scientific research. Furthermore, we visualize the current level of scientific research across all disciplines from a global perspective. © 2020, Springer Nature Switzerland AG.
- Authors: Ren, Jing , Lee, Ivan , Wang, Lei , Chen, Xiangtai , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Kyoto, Japan, 30 November to 1 December 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12504 LNCS, p. 416-422
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- Description: Academic capacity is a common way to reflect the educational level of a country or district. The aim of this study is to explore the difference between the scientific research level of institutions and countries. By proposing an indicator named Citation-weighted Research Complexity Index (CRCI), we profile the academic capacity of universities and countries with respect to research complexity. The relationships between CRCI of universities and other relevant academic evaluation indicators are examined. To explore the correlation between academic capacity and economic level, the relationship between research complexity and GDP per capita is analysed. With experiments on the Microsoft Academic Graph data set, we investigate publications across 183 countries and universities from the Academic Ranking of World Universities in 19 research fields. Experimental results reveal that universities with higher research complexity have higher fitness. In addition, for developed countries, the development of economics has a positive correlation with scientific research. Furthermore, we visualize the current level of scientific research across all disciplines from a global perspective. © 2020, Springer Nature Switzerland AG.
Web of scholars : a scholar knowledge graph
- Liu, Jiaying, Ren, Jing, Zheng, Wenqing, Chi, Lianhua, Lee, Ivan, Xia, Feng
- Authors: Liu, Jiaying , Ren, Jing , Zheng, Wenqing , Chi, Lianhua , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 p. 2153-2156
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- Description: In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM.
- Authors: Liu, Jiaying , Ren, Jing , Zheng, Wenqing , Chi, Lianhua , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 p. 2153-2156
- Full Text:
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- Description: In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM.
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