The 14th International Conference on Knowledge Science ...

The 14th International Conference on Knowledge Science, Engineering and

Management (KSEM 2021)

August 14-16, 2021 Tokyo, Japan

Conference Program and Information Booklet

Organized By KSEM 2021 Committee

Sponsored By Springer

Waseda University Longxiang High Tech Group Inc.

North America Chinese Talents Association

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目录

KSEM 2021 PROGRAM AT A GLANCE ................................................................................................... - 3 -

KSEM 2021 KEYNOTES ......................................................................................................................... - 4 -

KSEM 2021 KEYNOTES ......................................................................................................................... - 5 -

KSEM 2021 KEYNOTES ......................................................................................................................... - 6 -

TECHNICAL PROGRAM ......................................................................................................................... - 7 -

Table of Contents

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Saturday, August 14th, 2021

Room A Room B Room C

8:00-8:45 Conference Preparing and Online Facility Tuning

8:45 - 9:00 Opening

9:00 - 9:55 Keynote by Prof. Sun-Yuan Kung

9:55 – 10:00 Break

10:00 - 10:55 Keynote by Prof. Qiang Yang

10:55 –11:00 Break

11:00 –11:55 Keynote by Prof. Pierangela Samarati

11:55 –13:30 Break

13:30 –14:30 KSEM Volume 1 – Session 1 KSEM Volume 2 – Session 1 KSEM Volume 3 – Session 1

14:30 –15:30 KSEM Volume 1 – Session 2 KSEM Volume 2 – Session 2 KSEM Volume 3 – Session 2

Sunday, August 15th, 2021


9:00-10:30 Internal meeting

10:30 -11:30 KSEM Volume 1 - Session 3 KSEM Volume 2 - Session 3 KSEM Volume 3 - Session 3

11:30-12:30 KSEM Volume 1 - Session 4 KSEM Volume 2 - Session 4 KSEM Volume 3 - Session 4

12:30-13:30 Break

13:30:14:30 KSEM Volume 1 - Session 5 KSEM Volume 2 - Session 5 KSEM Volume 3 - Session 5

14:30 - 15:30 KSEM Volume 1 - Session 6 KSEM Volume 2 - Session 6 KSEM Volume 3 - Session 6

Monday, August 16th, 2021



10:00-11:00 KSEM Volume 1 - Session 8 KSEM Volume 2 - Session 8 KSEM Volume 3 - Session 8

11:00 - 11:20 Break


12:20 - 13:30 Break


Registration: Online Registration System (http://www.cloud-conf.net/ksem21/) Presentation Online Rooms: Zoom (https://zoom.us/) Virtual Conference Link: https://ntu-sg.zoom.com.cn/j/92466397240 Important Notice: Due to the outbreak of COVID-19, this year the KSEM 2021 will be a virtual conference online. For all participants, please do notice all the time mentioned in this booklet is based on the time zone of east USA which is Eastern Daylight Time (EDT), UTC -4.

KSEM 2021 Program at a Glance

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Aug. 14th, 2021, 9:00, Room A

Topic: On Regressive Neural Architectural Search (RNAS)

Prof. Sun-Yuan Kung Princeton University, USA

Life Fellow of IEEE

Bio: S.Y. Kung, Life Fellow of IEEE, is a Professor at Department of Electrical Engineering in Princeton University. His research areas include machine learning, data mining, systematic design of (deep-learning) neural networks, statistical estimation, VLSI array processors, signal and multimedia information processing, and most recently compressive privacy. He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society. He was elected to Fellow in 1988 and served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a recipient of IEEE Signal Processing Society's Technical Achievement Award for the contributions on "parallel processing and neural network algorithms for signal processing" (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994); a recipient of IEEE Signal Processing Society's Best Paper Award for his publication on principal component neural networks (1996); and a recipient of the IEEE Third Millennium Medal (2000). Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. He served as the first Associate Editor in VLSI Area (1984) and the first Associate Editor in Neural Network (1991) for the IEEE Transactions on Signal Processing. He has authored and co-authored more than 500 technical publications and numerous textbooks including "VLSI Array Processors", Prentice-Hall (1988); "Digital Neural Networks", Prentice-Hall (1993) ; "Principal Component Neural Networks", John-Wiley (1996); "Biometric Authentication: A Machine Learning Approach", Prentice-Hall (2004); and "Kernel Methods and Machine Learning”, Cambridge University Press (2014). Abstract: We have recently witnessed unprecedented proliferation of deep learning architectures with impressive performances superseding the state-of-the-arts. The task of optimizing the network parameters is usually handled by BP learning, often with great success. In contrast, the task of optimizing such network structure is usually left to trial-and-error. Moreover, it could be costly if we must start from scratch a brand new round of architectural search for new applications. Needless to say, it is highly desirable that if we could automate such an architecture learning task just like we learn the network parameters. This leads us to "Neural Architecture Search" (NAS), the process of automating architecture engineering. This talk starts by noting the vital roles of dimensionality in NAS. We shall review the curse/blessing of dimensionality (both depth and width) in deep learning networks. We shall then compare the (very different) design principles behind PNAS (Progressive NAS) and RNAS (Regressive NAS) and explain why do we favor RNAS over PNAS. This is especially so when we harness the vital roles RNAS to mitigate the aforementioned curse of dimensionality. In this talk, we shall address two technical areas related to the RNAS research: 1. Deep Compression: It is natural to augment the BP-Learning with a structural learning paradigm, leading to a X-Learning strategy to jointly learn the structure and parameters of the learning models. Based on LASSO-type (i.e. L0-norm) regression we derive a notion of Deleterious Neurons (DNs). This will ultimately lead to the proposed X-learning paradigm where deleterious links (DLs) will be gradually trimmed so as to reach an improved network structure. As to be demonstrated during the talk, X-learn is applicable to both types of application scenarios: Classification-type, e.g. CIFAR or ImageNet. and Regression-type, e.g. super-resolution (SR) hetero-encoder. This talk will also highlight our on-going development on XNAS: an reinforcement-learning autonomous NAS. 2. Data Compression: For data compression sectors, we have developed a dimension-reduction method called Regression Component Analysis (RCA), with closed-form error analysis. RCA is intimately related to PCA and RCA. For example, while PCA has already proven adequate for auto-encoder, we will need to resort to RCA in order to handle the (more general) hetero-encoder applications, e.g. SR imaging. As another example, we note that DCA and RCA are respectively the only closed-form mathematical tools for subspace analysis, the former for classification and the latter for regression. Note further that DCA has already enjoyed great success in visualization of data from different class labels, an emergent important research field in big data analysis. By the same token, RCA's promising roles for big data analyses can be naturally anticipated as well.

KSEM 2021 Keynotes

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Aug. 14th, 2021, 10:00, Room A

Topic: Solving the Data-silo and User-privacy

Challenges via Federated Learning

Prof. Qiang Yang Hong Kong University of Science and Technology, China

Fellows of IEEE, ACM, AAS

Bio: Qiang Yang is a chair professor at Hong Kong University of Science and Technology, Computer Science and Engineering Department. He heads the AI group at WeBank, and is a member of the Canadian Academy of Engineering. His research interests are federated learning, transfer learning and AI planning. He has published extensively including the books “Transfer Learning” (Cambridge University Press), “Federated Learning” (Morgan Claypool Publishers) and “Intelligent Planning” (Springer). He is a fellow of AAAI, IEEE, ACM, AAAS, etc. Abstract: AI is advancing by leaps and bounds in learning algorithm development, but AI has many challenges when put to practice. One of the major challenges faced by AI is the serious lack of data, which has led to the inability of many good algorithm models to be effectively applied. In this talk, I will present federated learning as a solution designed to connect data silos while protecting user privacy and provide security. I will illustrate some theoretical advances and practical applications.

KSEM 2021 Keynotes

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Aug. 14th, 2021, 11:00, Room A

Topic: Data security and privacy in emerging scenarios

Prof. Pierangela Samarati

Università degli Studi di Milano, Italy Fellow of IEEE

Bio: Pierangela Samarati is a Professor at the Department of Computer Science of the Università degli Studi di Milano, Italy. Her main research interests are on data and applications security and privacy, especially in emerging scenarios. She has been Computer Scientist at SRI, CA (USA) and visiting researcher at Stanford University, CA (USA), and at George Mason University, VA (USA). She is the chair of the IEEE Systems Council Technical Committee on Security and Privacy in Complex Information Systems (TCSPCIS), of the ERCIM Security and Trust Management Working Group (STM), and of the ACM Workshop on Privacy in the Electronic Society (WPES). She is ACM Distinguished Scientist (named 2009) and IEEE Fellow (named 2012). She has received the ESORICS Outstanding Research Award (2018), the IEEE Computer Society Technical Achievement Award (2016), the IFIP WG 11.3 Outstanding Research Contributions Award (2012), and the IFIP TC11 Kristian Beckman Award (2008). http://www.di.unimi.it/samarati/ Abstract: The rapid advancements in Information and Communication Technologies (ICTs) have been greatly changing our society, with clear societal and economic benefits. Mobile technology, Cloud, Big Data, Internet of things, services and technologies that are becoming more and more pervasive and conveniently accessible, towards to the realization of a smart society. At the heart of this evolution is the ability to collect, analyze, process and share an ever-increasing amount of data, to extract knowledge for offering personalized and advanced services. A major concern, and potential obstacle, towards the full realization of such evolution is represented by security and privacy issues. As a matter of fact, the (actual or perceived) loss of control over data and potential compromise of their confidentiality can have a strong detrimental impact on the realization of an open framework for enabling collection, processing, and sharing of data, typically stored or processed by external cloud services. In this talk, I will illustrate some security and privacy issues arising in emerging scenarios, focusing in particular on the problem of managing data while guaranteeing confidentiality and integrity of data stored or processed by external providers.

KSEM 2021 Keynotes

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Technical Program

The 14th International Conference on Knowledge Science, Engineering and Management (KSEM 2021)

KSEM Volume 1 – Session 1: Aug. 14th, 2021, 13:30, Room A Online Session

• Qingchao Zhao, Jing Yang, Zhengkui Wang, Yan Chu, Wen Shan and Isfaque Tuhin. Clustering Massive-categories and Complex Documents via Graph Convolutional Network.

• Yunke Zhang, Zhiwei Yang, Bo Yu, Hechang Chen, Yang Li and Xuehua Zhao. Structure-enhanced Graph Representation Learning for Link Prediction in Signed Networks.

• Jianbin Li, Ketong Qu, Jingchen Yan, Liting Zhou and Long Cheng. TEBC-Net: An effective relation extraction approach for simple question answering over knowledge graphs.

• Wenying Feng, Daren Zha, Xiaobo Guo, Yao Dong and Yuanye He. Representing Knowledge Graphs with Gaussian Mixture Embedding.

• Xiaohan Zhang, Xinning Zhu, Jie Wu, Zheng Hu and Chunhong Zhang. A Framework of Data Fusion through Spatio-temporal Knowledge Graph.

KSEM Volume 1 – Session 2 Aug. 14th, 2021, 14:30, Room A Online Session

• Xinyi Xu, Yan Tang and Zhuoming Xu. SEGAR: Knowledge Graph Augmented Session-based Recommendation.

• Yue Tang and Haizhou Du. Symbiosis: A Novel Framework for Integrating Hierarchies from Knowledge Graph into Recommendation System.

• Mengdan Wang, Chao Peng, Rui Yang, Chenchao Wang, Yao Chen and Xiaohua Yu. GASKT: A Graph-based Attentive Knowledge-Search Model for Knowledge Tracing

• Renjie Zhu, Ping Wei, Sheng Li, Zhaoxia Yin, Xinpeng Zhang and Zhenxing Qian. Fragile Neural Network Watermarking with Trigger Image Set

• Yashen Wang and Huanhuan Zhang. Introducing Graph Neural Networks for Few-Shot Relation Prediction in Knowledge Graph Completion Task

• Cheng Hu, Kui Xiao, Zesong Wang, Shihui Wang and Qifeng Li. Extracting Prerequisite Relations among Wikipedia Concepts using the Clickstream Data.


• Yuejia Wu and Jiantao Zhou. EN-DIVINE: An Enhanced Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning.

• Hui Tang, Xun Liang, Bo Wu, Zhenyu Guan, Yuhui Guo and Xiangping Zheng. Graph Ensemble Networks for semi-supervised embedding learning.

• Cong Ding, Xiao Wei, Yongqi Chen and Rui Zhao. Graph attention mechanism with cardinality preservation for knowledge graph completion.

• Tingting Tang, Wei Liu, Weimin Li, Jinliang Wu and Haiyang Ren. Event Relation Reasoning Based on Event Knowledge Graph.

• Yao Dong, Xiaobo Guo, Ji Xiang, Kai Liu and Zhihao Tang. HyperspherE: An Embedding Method for Knowledge Graph Completion Based on Hypersphere


• Ding Sun, Zhen Huang, Dongsheng Li, Xiangyu Ye and Yilin Wang. Improved Partitioning Graph Embedding Framework for Small Cluster.

• Jiachuan Li, Aimin Li and Teng Liu. Feature Interaction Convolutional Network for Knowledge Graph Embedding.

• Xiuting Song, Han Zhang and Luyi Bai. Entity Alignment between Knowledge Graphs Using Entity Type Matching.

• Qing Zhong and Yan Tang. Chinese Named Entity Recognition Based on Gated Graph Neural Network.

• Xi Wang, Chuantao Yin, Xin Fan, Si Wu and Lan Wang. An IoT Ontology Class Recommendation Method Based on Knowledge Graph.

• Zekun Li, Nianwen Ning, Chengcheng Peng and Bin Wu. Dependency Parsing Representation Learning for Open Information Extraction.


• Xingwang Wang and Tingting Yu. Research on Innovation Trends of AI Applied to Medical Instruments Using Informetrics Based on Multi-Sourse Information.

Technical Program

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• Ze Yin, Yue Deng, Fan Zhang, Zheng Luo, Peican Zhu and Chao Gao. A Semi-supervised Multi-objective Evolutionary Algorithm for Multi-layer Network Community Detection.

• Shi Peng, Yong Zhang, Yuanfang Yu, Haoyang Zuo and Kai Zhang. Named Entity Recognition Based on Reinforcement Learning and Adversarial Training.

• Weipeng Cao, Shengdong Li, Cheng-Chao Huang, Yuhao Wu, Qiang Wang, Dachuan Li and Ye Liu. An Ensemble Fuzziness-based Online Sequential Learning Approach and Its Application

• Arpad Kerestely, Alexandra Baicoianu and Razvan Bocu. A Research Study on Running Machine Learning Algorithms on Big Data with Spark.


• Fan Zhang, Junyou Zhu, Zheng Luo, Zhen Wang, Li Tao and Chao Gao. Community Detection In Dynamic Networks: A Novel Deep Learning Method.

• Xiaowu Zhang and Li Li. Attentional Neural Factorization Machines for Knowledge Tracing. • Di Qiao, Wu Yang and Wei Wang. Node-Image CAE：A Novel Embedding Method via

Convolutional Auto-Encoder and High-Order Proximities. • Jing Liao and Zhixiang Yi. A Deep Learning Model Based on Neural Bag-of-words Attention

for Sentiment Analysis. • Ziye Zhu, Yu Wang and Yun Li. TroBo: A Novel Deep Transfer Model for Enhancing Cross-

project Bug Localization. • Junchao Lv, Linjiang Zheng, Longquan Liao and Xin Chen. Ride-Sharing Matching of

Commuting Private Car using Reinforcement Learning.


• Ya Wang, Cungen Cao, Yuting Cao and Shi Wang. A Property-based Method for Acquiring Commonsense Knowledge.

• Yibing Zhao, Wenjun Ma, Yuncheng Jiang and Jieyu Zhan. A MOOCs Recommender System Based on User’s Knowledge Background.

• Bo Li, Bin Chen, Yunxiao Wang, Tao Dai, Shutao Xia, Yong Jiang and Maowei Hu. Knowledge Distillation via Channel Correlation Structure.

• Mingming Zheng, Yanquan Zhou and Qingyao Cui. Hierarchical Policy Network with multi-agent for Knowledge Graph Reasoning Based on Reinforcement Learning.

• Nan Zhang and Li Li. Knowledge Tracing with Exercise-Enhanced Key-Value Memory Networks.

• Shihong Jiang, Zheng Luo, Ze Yin, Zhen Wang, Songxi Wang and Chao Gao. Identification of Critical Nodes in Urban Transportation Network through Network Topology and Server Routes.

KSEM Volume 1 – Session 8

Aug. 16th, 2021, 10:00, Room A Online Session • Dou Hu, Lingwei Wei, Wei Zhou, Xiaoyong Huai, Zhiqi Fang and Songlin Hu. PEN4Rec:

Preference Evolution Networks for Session-based Recommendation • Yuhong He and Yan Tang. A Neural Language Understanding for Dialogue State Tracking. • Nan Qiu, Boyu Gao, Feiran Huang, Huawei Tu and Weiqi Luo. Incorporating Global

Context into Multi-task Learning for Session-based Recommendation. • Tao Wang. Multi-hop Learning promote Cooperation in Multi-agent Systems. • Weidong Zou, Yuanqing Xia, Meikang Qiu and Weipeng Cao. Dense Incremental Extreme

Learning Machine with Accelerating Amount and Proportional Integral Differential. • Jing Yang, Gaojin Fan, Kai Xie, Qiqi Chen and Aiguo Wang. Additive noise model structure

learning based on rank statistics.

KSEM Volume 1 – Session 9 Aug. 16th, 2021, 11:20, Room A Online Session • Mulin Xu. FedPS: Model Aggregation with Pseudo Samples. • Emna Ben Abdallah, Khouloud Boukadi and Rima Grati. Towards a modular ontology for

cloud consumer review mining. • Xinyu Jiang, Chongyang Shi, Shufeng Hao, Dequan Yang and Chaoqun Feng. Rethinking

the Information inside Documents for Sentiment Classification. • Xuxing Liu, Shanxiong Chen and Xiaoqin Tang. Learning a Similarity Metric

Discriminatively with Application to Ancient Character Recognition. • Jingyi Liu, Yanyan Zhao, Limin Liu and Shijie Jia. Exploring Sequential and Collaborative

Contexts for Next Point-of-Interest Recommendation. • Yubao Tang, Zhezhou Li, Cong Cao, Fang Fang, Yanan Cao, Yanbing Liu and Jianhui Fu.

Knowledge-based Diverse Feature Transformation For Few-shot Relation Classification.

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• Shaolin Zhu, Chenggang Mi and Linlin Zhang. Inducing bilingual word representations for non-isomorphic spaces by an unsupervised way.

• Zhiyuan Wu, Yu Jiang, Minghao Zhao, Chupeng Cui, Zongmin Yang, Xinhui Xue and Hong Qi. Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer.

• Gang Qiu, Xiaoli Yu, Liping Jiang and Baoying Ma. Text-aware recommendation model based on multi-attention neural network.

• Xiaotong Li, Yan Tang, Yuan Yuan and Yingpei Chen. Predicting User Preferences via Heterogeneous Information Network and Metric Learning.

• Hejun Wang, Hongjun Dai, Meikang Qiu and Meiqin Liu. Optimization of Remote Desktop with CNN Based Image Compression Model.

KSEM Volume 2 – Session 1 Aug. 14th, 2021, 13:30, Room B Online Session

• Zhaoyun Ding, Kai Liu, Wenhao Wang and Bin Liu. A Semantic Textual Similarity Calculation Model Based on Pre-training Model.

• Xiuyue Zeng, Jiang Zhong, Chen Wang and Cong Hu. Chinese Relation Extraction with Flat-Lattice Encoding and Pretrain-Transfer Strategy.

• Qingqing Li, Zhen Huang, Yong Dou and Ziwen Zhang. A Framework of Data Augmentation While Active Learning for Chinese Named Entity Recognition.

• Wei Zheng, Hongxu Hou, Nier Wu and Shuo Sun. Bayesian belief network Model using Sematic Concept for Expert Finding.

• Benjamin Mensa-Bonsu, Tao Cai, Tresor Y. Koffi and Dejiao Niu. The Novel Efficient Transformer for NLP.


• Dong Cheng, Hui Song, Xianglong He and Bo Xu. Joint Entity and Relation Extraction for Long Text.

• Aleksandr Perevalov and Andreas Both. Improving Answer Type Classification Quality Through Combined Question Answering Datasets.

• Wei Li and Li Li. Combining Knowledge with Attention Neural Networks for Short Text Classification.

• Zhenyu Zhang, Tao Guo, Ling Jiang and Manchang Gu. A Dialogue Contextual Flow Model for Utterance Intent Recognition in Multi-turn Online Conversation.

• Rui Zhao, Xiao Wei, Cong Ding and Yongqi Chen. Hierarchical Multi-label Text Classification: Self-adaption Semantic Awareness Network Integrating Text Topic and Label Level Information.

• Yuanming Zhang, Tianyu Gao, Jiawei Lu, Zhenbo Cheng and Xiao Gang. Adaptive Entity Alignment for Cross-lingual Knowledge Graph.


• Phong Do, Nhat Nguyen, Tin Huynh, Kiet Nguyen, Anh Nguyen and Ngan Nguyen. Sentence Extraction-Based Machine Reading Comprehension for Vietnamese.

• Wenhan Wu, Yongxin Zhao, Chao Peng, Qin Li and Yongjian Li. Analyzing and Recommending Development Order based on Design Class Diagram.

• Ying Li, Ming Ye and Qian Hu. HCapsNet: A Text Classification Model Based On Hierarchical Capsule Network.

• Zhipeng Wang and Danfeng Yan. Sentence Matching With Deep Self-Attention and Co-Attention Features.

• Liping Hua, Qinhui Chen, Zelin Huang, Hui Zhao and Gang Zhao. Not Only the Contextual Semantic Information：A Deep Fusion Sentimental Analysis Model towards Extremely Short Comments.


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• Qingyao Cui, Yanquan Zhou and Mingming Zheng. Sememes-based Framework for Knowledge Graph Embedding with Comprehensive-Information.

• Fan Zhang, Rui Li, Ke Xu and Hongguang Xu. Similarity-Based Heterogeneous Graph Attention Network for Knowledge-Enhanced Recommendation.

• Hui Chen, Chuantao Yin, Xin Fan, Lei Qiao, Wenge Rong and Xiong Zhang. Learning path recommendation for MOOC platforms based on a knowledge graph.

• Nidhi Goyal, Niharika Sachdeva and Ponnurangam Kumaraguru. Spy The Lie: Fraudulent Jobs Detection in Recruitment Domain using Knowledge Graphs.

• Yuhui Ye, Linjiang Zheng, Yiling Chen and Longquan Liao. Discovering Stable Ride-sharing Groups for Commuting Private Car using Spatio-temporal Semantic Similarity.


• Tianyu Gao, Yuanming Zhang, Mengni Li, Jiawei Lu, Zhenbo Cheng and Gang Xiao. Representation Learning of Knowledge Graph with Semantic Vectors.

• Yihong Zhang, Masumi Shirakawa and Takahiro Hara. An Automatic Method for Understanding Political Polarization through Social Media.

• Michael Paris and Robert Jäschke. Evaluating dataset creation heuristics for concept detection in web pages using BERT.

• Yuhan Wang, Qing Xie, Lin Li and Yongjian Liu. An Empirical Study on Effect of Semantic Measures in Cross-domain Recommender System in User Cold-start Scenario.

• Binglong Ye, Shengyu Mao, Pengyi Hao, Wei Chen and Cong Bai. Community Enhanced Course Concept Recommendation in MOOCs with Multiple Entities.


• Minjie Ding, Mingang Chen, Wenjie Chen and Lizhi Cai. English Cloze Test Based on BERT.

• Linhui Feng, Linbo Qiao, Yi Han, Zhigang Kan, Yifu Gao and Dongsheng Li. Syntactic Enhanced Projection Network for Few-shot Chinese Event Extraction.

• Weinan He and Zhanhao Xiao. Towards Solving the Winograd Schema Challenge: Model-Free, Model-Based and a Spectrum in Between.

• Min Lu, Feilong Bao and Guanglai Gao. Panoptic-DLA: Doucument Layout Analysis of Historical Newspapers Based on Proposal-Free Panoptic Segmentation Model.

• Kedian Mu. The Modularity of Inconsistent Knowledge Bases with Application to Measuring Inconsistency.

• Yunlei Zhang, Xiangyao Ma, Huayu Guan and Ling Wang. Construction and Analysis of Cross-Regional Emergency Collaboration Network Model.


• Jiayan Wang, Ziang Chen, Juchuan Niu and Yonggang Zhang. AABC:ALBERT-BiLSTM-CRF combining with Adapters.

• Rahma Kadri, Mohamed Tmar and Bassem Bouaziz. Alzheimer’s Disease Prediction Using EfficientNet and Fastai.

• Runmin Wang, Yuanlin Yang and Guangyang Han. A Label Noise Robust Cross-Modal Hashing Approach.

• Andreea Bianca Lixandru, Sebastian Gorobievschi and Alexandra Baicoianu. Acoustic Modeling for Indoor Spaces Using Ray-Tracing Method.

• Luong Luc Phan, Phuc Huynh Pham, Kim Thi Thanh Nguyen, Sieu Khai Huynh, Tham Thi Nguyen, Luan Thanh Nguyen, Tin Van Huynh and Kiet Van Nguyen. SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence.


• Zhaoyang Wang and Shaowei Pan. An Improved Convolutional Neural Network Based on Noise Layer.

• Minzhong Luo and Yu Shan. Traffic Route Planning in Partially Observable Enviroment Using Actions Group Representation.

• Zhu Tongyu and Tong Zhiwei. FOBA: Flight Operation Behavior Analysis Based On Hierarchical Encoding.

• Zehao Yu. An Event Detection Method Combining Temporal Dimension and Position Dimension.

• Ning Jiang, Jialiang Tang, Wenxin Yu and Jinjia Zhou. Local Feature Normalization


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• Min Gan and Li Zhang. Q-learning with Fisher Score for Feature Selection of Large-scale Data Sets.

• Jianwen Sun, Jianpeng Zhou, Kai Zhang, Qing Li and Zijian Lu. Collaborative Embedding for Knowledge Tracing.

• Xuerui Lv and Li Zhang. Residual gated recurrent unit-based stacked network for stock trend prediction from limit order book.

• Tongyu Zhu, Peng Ling, Zhiyuan Chen, Dongdong Wu and Ruyan Zhang. A Social Attribute Inferred Model Based on Spatio-temporal Data.

• Maja Pavlovic, Yaxin Bi and Peter Nicholl. Extracting Anomalous Pre-Earthquake Signatures from Swarm Satellite Data using EOF and PC analysis.


• Jie Liu, Jiaye Wu and Xudong Luo. Chinese Judicial Abstracting Based on Short Sentence Extraction and GPT-2.

• Chong Zhang, Zhiyuan Wang, Liuqing Yang, Xiao-Yang Liu and Ling Jiang. Domain-Specific Sentence Encoder for Intention Recognition in Large-Scale Shopping Platforms.

• Chang Ni, Wei Liu, Weimin Li, Jinliang Wu and Haiyang Ren. Chinese Event Detection Based on Event Ontology and Siamese Network.

• Juan Chen, Ang Gao, Haiyang Jia, Yuanteng Xu and Xianglu Zhou. Interval Occlusion Calculus with Size Information.

• Guojie Zhao, Yupeng Zhang, Peichu Liu, Haoen Wu and Mingyang Cui. Accurate and robust RGB-D visual odometry based on point and line features

• Fiachra Merwick, Yaxin Bi and Peter Nicholl. Performance Evaluation of Multi-class Sentiment Classification using Deep Neural Network Models Optimised for Binary Classification.

KSEM Volume 3 – Session 1 Aug. 14th, 2021, 13:30, Room C Online Session

• Zhishen Nie, Ying Lin, Meng Yan, Yifan Cao and Shenfu Ning. An adversarial training method for improving model robustness in unsupervised domain adaptation.

• Ming Liu, Jianxin Liao, Jingyu Wang, Qi Qi, Haifeng Sun and Xiaoyuan Fu. Context-aware anomaly detection in Attributed Networks.

• Yang Cao and Shi Wang. An Efficient Hybrid Approach to Detecting and Correcting Auxiliary Word Errors in Chinese Text.

• Haozhe Zhao and Guozheng Rao. Traffic Accident Prediction Methods Based on Multi-factor Models.

• Rida Miraj and Masaki Aono. Combining BERT and Multiple Embedding Methods with the Deep Neural Network for Humor Detection

• Guangxian Lyu, Peng Liu, Zhengdong Ren, Wang Zhou, Jun Wang and Yu Huang. QBT: Efficient and Flexible Resource Allocation Method for Data Center of State Grid Scenario.


• Beibei Ruan and Cui Zhu. An Efficient Link Prediction Model in Dynamic Heterogeneous Information Networks Based on Multiple Self-attention.

• Hanming Zheng, Ling Luo and Goce Ristanoski. A Clustering-prediction Pipeline for Customer Churn Analysis.

• Xinxin Liao, Mingyan Wu, Heyan Chai, Shuhan Qi, Xuan Wang and Qing Liao. Fine-grained Unbalanced Interaction Network for Visual Question Answering.

• Wenqi Li, Hui Kang, Tie Feng, Jiahui Li, Zhiru Yue and Geng Sun. Swarm Intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method.

• Liang Zhu, Xinfeng Li, Yonggang Wei, Qin Ma and Weiyi Meng. Integrating Real-time Entity Resolution with Top-N Join Query Processing.


• Shuang Chen and Li Li. Incorporating Question Information to Enhance the Performance of Automatic Short Answer Grading.

• Zhaochen Li and Kedian Mu. Integrating Task Information into Few-Shot Classifier by Channel Attention.

• Xiaoyun Han, Zhen Huang, Menglong Lu, Dongsheng Li and Jinyan Qiu. Rumor Verification on Social Media with Stance-Aware Recursive Tree.

• Chao Liu, Xintong Wei, Min Yu, Gang Li, Xiangmei Ma, Jianguo Jiang and Weiqing Huang. Aspect and Opinion Terms Co-Extraction using Position-Aware Attention and Auxiliary Labels.

• Guoguo Ai, Hui Yan, Jian Yang and Xin Li. Beyond Laplacian Smoothing for Semi-Supervised Community Detection.

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• Jianguo Jiang, Qiang Liu, Min Yu, Gang Li, Mingqi Liu, Chao Liu and Weiqing Huang. Landscape-Enhanced Graph Attention Network for Rumor Detection.

• Jan Vanthienen and Vedavyas Etikala. An Overview of Methods for Acquiring and Generating Decision Models.

• Yan Chu, Zhengkui Wang, Lina Wang, Qingchao Zhao and Wen Shan. Fine-Grained Image Classification Based on Target Acquisition and Feature Fusion.

• Linming Zhang, Wenzhong Li, Zhijie Zhang, Qingning Lu, Ce Hou, Peng Hu, Tong Gui and Sanglu Lu. LogAttn: Unsupervised Log Anomaly Detection with an AutoEncoder based Attention Mechanism.

• Xuan Zang, Bo Yang, Xueyan Liu and Anchen Li. DNEA: Dynamic Network Embedding Method for Anomaly Detection.

• Mourad Ellouze, Seifeddine Mechti and Lamia Hadrich Belguith. Approach based on ontology and machine learning for identifying causes affecting personality disorder disease on Twitter.


• Yashen Wang and Huanhuan Zhang. Adversarial Constraint Evaluation on Biomedical Text Mining.

• Hao Liu, Fan Zhang, Yi Fan, Junyou Zhu, Zhen Wang and Chao Gao. Enhanced Self-node Weights Based Graph Convolutional Networks for Passenger Flow Prediction.

• Gaigai Tang, Long Zhang, Feng Yang, Lianxiao Meng, Weipeng Cao, Meikang Qiu, Shuangyin Ren, Lin Yang and Huiqiang Wang. Interpretation of Learning-based Automatic Source Code Vulnerability Detection Model Using LIME.

• Zhengyang Mu, Qi Qi, Jingyu Wang, Haifeng Sun and Jianxin Liao. Efficient Depth Completion Network based on Dynamic Gated Fusion.

• Chenkai Guo, Yapeng Zi and Wei Ren. A blockchain based framework for smart greenhouse data management.

• Haiqiang Wang, Xuyuan Dong, Zheng Luo, Junyou Zhu, Peican Zhu and Chao Gao. Medication Combination Prediction via Attention Neural Networks with Prior Medical Knowledge.


• Yutao Chen, Yuxuan Zhang, Zhongrui Huang, Zhenyao Luo and Jinpeng Chen. CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA.

• Juan Chen, Siqi Liu, Ang Gao, Haiyang Jia, Yifan Shao and Wenxin Tang. Image super-resolution based on residual block dense connection.

• Jian Hu, Qing Ding and Wenyu Zhang. EGIM: Evolution Graph based Interest Modeling for Click-Through Rate Prediction.

• Yu Liang, Arin Chaudhuri and Haoyu Wang. Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data.

• Lingyao Yan, Chuantao Yin, Hui Chen, Wenge Rong, Zhang Xiong and Bertrand David. Learning Resource Recommendation in E-learning Systems based on Online Learning Style


• Jiaye Wu and Xudong Luo. Alignment-Based Graph Network for Judicial Examination Task.

• Qiuyue Li, Nianwen Ning, Bin Wu and Wenying Guo. Embedding-based Network Alignment Using Neural Tensor Networks.

• Rania Khaskhoussy and Yassine Ben Ayed. Detecting Parkinson's disease according to gender using speech signals.

• Arpad Kerestely and Sabin Tabirca. Theoretical Study of Exponential Best-Fit: Modeling hCG for Gestational Trophoblastic Disease.

• Qi Kong, Liangliang Zhang and Xin Xu. Lane Keeping Algorithm for Autonomous Driving via Safe Reinforcement Learning.


• Yue Zhang, Keke Gai, Yihang Wei and Liehuang Zhu. BS-KGS: Blockchain Sharding Empowered Knowledge Graph Storage.

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• Xuhao Lin and Shengsheng Wang. Faster Nonlocal UNet for Cell Segmentation in Microscopy Images.

• Junhao Wang. An Improved YOLO algorithm for object detection in all day scenarios. • Xiaochen Wang, Tingsong Xiao and Jie Shao. EMRM: Enhanced Multi-source Review-

based Model for Rating Prediction. • Yuwen Li, Hao Yin, Keke Gai, Liehuang Zhu and Qing Wang. Blockchain-as-a-Service

Powered Knowledge Graph Construction. • Meiquan Wang, Guangshun Li, Yue Zhang, Keke Gai and Meikang Qiu. An Edge

Trajectory Protection Approach Using Blockchain.


• Xin Liu and Jun Wu. Finetuned YOLOv3 For Getting Four Times The Detection Speed. • Hajer Ben Haj Ayech, Emna Ammar Elhadjamor and Sonia Ghannouchi. A Systematic

Approach for maintainable business Process Models. • Xiaoqin Tang and Xuxing Liu. Improved Evolution Algorithm that Guides the Direction of

Individual Mutation for Influence Maximization in Social Networks. • Shoukang Han, Neng Gao, Xiaobo Guo and Yiwei Shan. Incorporating Common

Knowledge and Specific Entity Linking Knowledge for Machine Reading Comprehension. • Su Pei, Ke Niu, Xueping Peng and Jingni Zeng. Readmission Prediction with Knowledge

Graph Attention and RNN-based Ordinary Differential Equations. • Xiaohui Wei, Nan Jiang, Xiaonan Wang and Hengshan Yue. Detecting SDCs in GPGPUs

through an Efficient Instruction Duplication Mechanism.


• Hui Zhao, Peng Su, Yihang Wei, Keke Gai and Meikang Qiu. GAN-enabled Code Embedding for Reentrant Vulnerabilities Detections.

• Ang Gao, Lingjiang Zheng, Zixu Wang, Xuanxuan Luo, Congjun Xie and Yuankai Luo. Attention based short-term metro passenger flow prediction.

• Saleha Noor, Yi Guo, Syed Hamad Hassan Shah and Habiba Halepoto. Thematic analysis of Twitter as a platform for knowledge management.

• Tianxiu Xie, Yue Zhang, Keke Gai and Lei Xu. Cross-chain-based Decentralized Identity for Mortgage Loans.

• Huiru Zhang, Guangshun Li, Yue Zhang, Keke Gai and Meikang Qiu. Blockchain-based Privacy-preserving Medical Data Sharing Scheme Using Federated Learning.

The 14th International Conference on Knowledge Science ...

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