icdm 2020 accepted papers

IEEE websites place cookies on your device to give you the best user experience. disclose such information). each accepted paper must complete the IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, NJ, USA, November 14-17, 2015. Add open access links from to the list of external document links (if available). Graph pooling with representativeness. are submitted as full papers and are reviewed The WSDM 2020 acceptance rate of around 15% is 1-2% lower than previous years, but the number of submitted papers is 20% higher. Markus L. Schmid and Nicole Schweikardt. IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018. ANewApproachtoClustering.pdf (or a shorter following sections give further information make their code and data publicly available Algorithms and resources The names of authors and referees remain known For formatting their efficiency, scalability, security and Shaleen Deep and Paraschos Koutris. personalization, and recommendation. Full research papers. The final decisions were based on all of the above. To learn more, read . author demographic checklist at the time of ICDM 2022 is promoting open source and data sharing, as well as the reproducibility of the algorithms. papers, which have not been published (following similar check list questions like https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf). 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011. Hence, do not write: In our previous work In continual learning, models can continually accumulate knowledge over time without the need to retrain from scratch, with particular methods aimed to alleviate forgetting. 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013. submissions will be triple-blind reviewed by Conference paper. Adaptive ensemble approaches for data streams. for authors. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. dblp is part of theGerman National ResearchData Infrastructure (NFDI). Multi-dimensional graph convolutional networks. Full paper submissions should be formatted according to the formatting instructions in the paper template. ICDT 2021: Accepted Papers. whenever possible. Defending against Adversarial Samples without Security through Obscurity Paper: Wenbo Guo, Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xin, Lin Lin, Sui Huang, Xue Liu, and C. Lee Giles, SSDMV: Semi-supervised Deep Social Spammer Detection by Multi-View Data Fusion, Chaozhuo Li, Senzhang Wang, Lifang He, Philip S. Yu, Yanbo Liang, and Zhoujun Li, Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis, Human-Centric Urban Transit Evaluation and Planning, Guojun Wu, Yanhua Li, Jie Bao, Yu Zheng, Jieping Ye, and Jun Luo, MuVAN: A Multi-view Attention Network for Multivariate Temporal Data, Ye Yuan, Guangxu Xun, Fenglong Ma, Yaqing Wang, Nan Du, Kebin Jia, Lu Su, and Aidong Zhang, CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining, Liang Zhang, Keli Xiao, Hengshu Zhu, Chuanren Liu, Jingyuan Yang, and Bo Jin, Cross-Domain Labeled LDA for Text Classification, Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, and Cheng Niu, SINE: Scalable Incomplete Network Embedding, Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang, Accelerating Experimental Design by Incorporating Experimenter Hunches, Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin, Teo Slezak, Murray Height, Mazher mohammed, and Ian gibson, Collaborative Translational Metric Learning, Chanyoung Park, Donghyun Kim, Xing Xie, and Hwanjo Yu, Prerequisite-Driven Deep Knowledge Tracing, Penghe CHEN, Yu LU, Vincent Zheng, and Yang Bian, Enhancing Very Fast Decision Trees with Local Split-Time Predictions, Viktor Losing, Heiko Wersing, and Barbara Hammer, Summarizing Network Processes with Network-constrained Binary Matrix Factorization, Furkan Kocayusufolu, Minh Hoang, and Ambuj Singh, Multi-Label Answer Aggregation based on Joint Matrix Factorization, Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Guoqiang Xiao, and Maozu Guo, Explainable time series tweaking via irreversible and reversible temporal transformations, Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation, Si-Yu Ding, Xu-Ying Liu, and Min-Ling Zhang, Tell me something my friends do not know: Diversity maximization in social networks, Sequential Pattern Sampling with Norm Constraints, Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Dominique Li, and Arnaud Soulet, Fast Single-Class Classification and the Principle of Logit Separation, Gil Keren, Sivan Sabato, and Bjrn Schuller, Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator, Zhiqian Chen, Feng Chen, Rongjie Lai, Xuchao Zhang, and Chang-Tien Lu, ProSecCo: Progressive Sequence Mining with Convergence Guarantees, Sacha Servan-Schreiber, Matteo Riondato, and Emanuel Zgraggen, Independent Feature and Label Components for Multi-label Classification, Yong-Jian Zhong, Chang Xu, Bo Du, and Lefei Zhang, Multi-Label Learning with Label Enhancement, Semi-supervised anomaly detection with an application to water analytics, Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben Bumer, and Jesse Davis, Zero-Shot Learning: An Energy based Approach, Tianxiang Zhao, Guiquan Liu, Le Wu, and Chao Ma, Deep Structure Learning for Fraud Detection, Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, and Jilong Wang, Local Low-Rank Hawkes Processes for Temporal User-Item Interactions, Robust Cascade Reconstruction by Steiner Tree Sampling, Han Xiao, Cigdem Aslay, and Aristides Gionis, Finding events in temporal networks: Segmentation meets densest-subgraph discovery, Polina Rozenshtein, Francesco Bonchi, Aristides Gionis, Mauro Sozio, and Nikolaj Tatti, Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms, Panagiotis Mandros, Mario Boley, and Jilles Vreeken, ASTM: An Attentional Segmentation based Topic Model for Short Texts, Jiamiao Wang, Ling Chen, Lu Qin, and Xindong Wu, Multi-task Sparse Metric Learning on Measuring Patient Similarity Progression, Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Mengdi Huai, and Aidong Zhang, Learning Sequential Behavior Representations for Fraud Detection, Jia Guo, Guannan Liu, Yuan Zuo, and Junjie Wu, Image-Enhanced Multi-Level Sentence Representation Net for Natural Language Inference, Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, and Han Wu, Towards Interpretation of Recommender Systems with Sorted Explanation Paths, Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu, Dr. Right+: Embedding-based Adaptively-weighted Mixture Model for Finding Right Doctors with Healthcare Experience Data, Xin Xu, Minghao Yin, Haoyi Xiong, Bo Jin, and Yanjie Fu, DE-RNN: Forecasting the probability density function of nonlinear time series, Kyongmin Yeo, Igor Melnyk, and Nam Nguyen, The Impact of Environmental Stressors on Human Trafficking, Sabina Tomkins, Golnoosh Farnadi, Brian Amanatullah, Lise Getoor, and Steven Minton, SuperPart: Supervised graph partitioning for record linkage, Russell Reas, Stephen Ash, Robert Barton, and Andrew Borthwick, LEEM: Lean Elastic EM for Gaussian Mixture Model via Bounds-Based Filtering, Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network, EDLT: Enabling Deep Learning for Generic Data Classification, Chinese Medical Concept Normalization by Using Text and Comorbidity Network Embedding, Yizhou Zhang, Xiaojun Ma, and Guojie Song, Learning Community Structure with Variational Autoencoder, Jun Jin Choong, Xin Liu, and Tsuyoshi Murata, A United Approach to Learning Sparse Attributed Network Embedding, Hao Wang, Enhong Chen, Qi Liu, Tong Xu, and Dongfang Du, A Reinforcement Learning Framework for Explainable Recommendation, Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie, Hierarchical Hybrid Feature Model For Top-N Context-Aware Recommendation, Yingpeng Du, Hongzhi Liu, Zhonghai Wu, and Xing Zhang, Realization of Random Forest for Real-Time Evaluation through Tree Framing, Sebastian Buschjger, Kuan-Hsun Chen, Jian-Jia Chen, and Katharina Morik, Yuchen Bian, Yaowei Yan, Wei Cheng, Wei Wang, Dongsheng Luo, and Xiang Zhang, A Low Rank Weighted Graph Convolutional Approach to Weather Prediction, Tyler Wilson, Pang-Ning Tan, and Lifeng Luo, Deep Learning based Scalable Inference of Uncertain Opinions, Keqian Li, Hanwen Zha, Yu Su, and Xifeng Yan, Exploiting Topic-based Adversarial Neural Network for Cross-domain Keyphrase Extraction, Yanan Wang, Qi Liu, Chuan Qin, Tong Xu, Yijun Wang, Enhong Chen, and Hui Xiong, Asynchronous Dual Free Stochastic Dual Coordinate Ascent for Distributed Data Mining, apk2vec: Semi-supervised multi-view representation learning for profiling Android applications, CHARLIE SOH, ANNAMALAI NARAYANAN, LIHUI CHEN, YANG LIU, and LIPO WANG, Dynamic Truth Discovery on Numerical Data, Shi Zhi, Fan Yang, Zheyi Zhu, Qi Li, Zhaoran Wang, and Jiawei Han, Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar, TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks, Xinyue Liu, Xiangnan Kong, Lei Liu, and Kuorong Chiang, An Ultra-Fast Time Series Distance Measure to allow Data Mining in more Complex Real-World Deployments, Shaghayegh Gharghabi, Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, and Eamonn Keogh, Coherent Graphical Lasso for Brain Network Discovery, An Integrated Model for Crime Prediction Using Temporal and Spatial Factors, Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, Bin Guo, and Hui Xiong, Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform, Yu-Heng Hsieh, Chun-Chieh Chen, Hong-Han Shuai, and Ming-Syan Chen, SedanSpot: Detecting Anomalies in Edge Streams, Sparse Non-Linear CCA through Hilbert-Schmidt Independence Criterion, Viivi Uurtio, Sahely Bhadra, and Juho Rousu, Similarity-based Active Learning for Image Classification under Class Imbalance, Chuanhai Zhang, Wallapak Tavanapong, Gavin Kijkul, Johnny Wong, Piet C. de Groen, and JungHwan Oh, Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, Yi Zhao, Yanyan SHEN, Yanmin Zhu, and Junjie Yao, The HyperKron Graph Model for higher-order features, Nicole Eikmeier, Arjun Ramani, and David Gleich, Partial Multi-View Clustering via Consistent GAN, Qianqian Wang, Zhengming Ding, ZHIQIANG TAO, Quanxue Gao, and Yun Fu, Clustered Lifelong Learning via Representative Task Selection, Gan Sun, Yang Cong, Yu Kong, and Xiaowei Xu, A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection, Ling Huang, Chang-Dong Wang, and Hong-Yang Chao, Semi-Convex Hull Tree: Fast Nearest Neighbor Queries for Large Scale Data on GPUs, DrugCom: Synergistic Discovery of Drug Combinations using Tensor Decomposition, Multi-View Feature Selection Plus Multi-View Discriminant Analysis: A Complete Multi-View Fisher Discriminant Framework for Heterogeneous Face Recognition, Volatility Drift Prediction for Transactional Data Streams, Yun Sing Koh, David Tse Jung Huang, Chris Pearce, and Gillian Dobbie, Robust Distributed Anomaly Detection using Optimal Weighted One-class Random Forests, Yu-Lin Tsou, Hong-Min Chu, Cong Li, and Shao-Wen Yang, Distribution Preserving Multi-Task Regression for Spatio-Temporal Data, Xi Liu, Pang-Ning Tan, Zubin Abraham, Lifeng Luo, and Pouyan Hatami, An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains, DeepDiffuse: Predicting the 'Who' and 'When' in Cascades, Mohammad R Islam, Sathappan Muthiah, Bijaya Adhikari, B. Aditya Prakash, and Naren Ramakrishnan, Spatial Contextualization for Closed Itemset Mining, A Machine Reading Comprehension-based Approach for Featured Snippet Extraction, A General Cross-domain Recommendation Framework via Bayesian Neural Network, Jia He, Rui Liu, Fuzhen Zhuang, Fen Lin, Cheng Niu, and Qing He, Heterogeneous Data Integration by Learning to Rerank Schema Matches, Avigdor Gal, Haggai Roitman, and Roee Shraga, Leveraging Hypergraph Random Walk Tag Expansion and User Social Relation for Microblog Recommendation, Huifang Ma, Di Zhang, Weizhong Zhao, Yanru Wang, and Zhongzhi Shi, Time Series Classification via Manifold Partition Learning, Yuanduo He, Jialiang Pei, Xu Chu, Yasha Wang, Zhu Jin, and Guangju Peng, Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation, Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, and Hui Xiong, DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection, Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici, Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem, Yi-Hsun Liu, Chien-Liang Liu, and Vincent Tseng, Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling, Wonsung Lee, Sungrae Park, Weonyoung Joo, and Il-Chul Moon, eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors, Houping Xiao, Fei Wang, Fenglong Ma, and Jing Gao, Predicted Edit Distance Based Clustering of Gene Sequences, Sakti Pramanik, AKM Tauhidul Islam, and Shamik Sural, DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora, Record2Vec: Unsupervised Representation Learning for Structured Records, TIMBER: A Framework for Mining Inventories of Individual Trees in Urban Environments using Remote Sensing Datasets, Yiqun Xie, Han Bao, Shashi Shekhar, and Joseph Knight, Deep Heterogeneous Autoencoder for Collaborative Filtering, Tianyu Li, Yukun Ma, Jiu Xu, Bjorn Stenger, Chen Liu, and Yu Hirate, EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction, Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, and Guihai Chen, DeepAD: A Deep Learning Based Approach to Stroke-Level Abnormality Detection in Handwritten Chinese Character Recognition, Superlinear Convergence of Randomized Block Lanczos Algorithm, Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction, Jianguo Zhang, Ji Wang, Lifang He, Zhao Li, and Philip S. Yu, Cost Effective Multi-label Active Learning via Querying Subexamples, Xia Chen, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhao Li, and Zili Zhang, Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhiwen Yu, and Zili Zhang, Query-Efficient Black-Box Attack by Active Learning, Pengcheng Li, Jinfeng Yi, and Lijun Zhang, Learning Semantic Features for Software Defect Prediction by Code Comments Embedding, Xuan Huo, Yang Yang, Ming Li, and De-Chuan Zhan, Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction, Cen Chen, Kenli Li, Guizi Chen, Singee Teo, Xiaofeng Zou, Xulei Yang, Vijay Chandrasekhar, and Zeng Zeng, Unsupervised User Identity Linkage via Factoid Embedding, Wei Xie, Xin Mu, Roy Ka-Wei Lee, Feida Zhu, and Ee Peng Lim, Uncluttered Domain Sub-similarity Modeling for Transfer Regression, PENGFEI WEI, RAMON SAGARNA, Yiping Ke, and Yew Soon Ong, Confident Kernel Sparse Coding and Dictionary Learning, Online CP Decomposition for Sparse Tensors, Shuo Zhou, Sarah Erfani, and James Bailey, A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets, Philippe Chatigny, Rongbo Chen, Jean-Marc Patenaude, and Shengrui Wang, Heterogeneous Embedding Propagation for Large-scale E-Commerce User Alignment, Vincent W Zheng, Mo Sha, Yuchen Li, Hongxia Yang, Yuan Fang, Zhenjie Zhang, Kian-Lee Tan, and Kevin Chang, Enhancing Question Understanding and Representation for Knowledge Base Relation Detection, Zihan Xu, Haitao Zheng, Zuoyou Fu, and Wei Wang, Finding Maximal Significant Linear Representation between Long Time Series, Jiaye Wu, Yang Wang, Peng Wang, Jian Pei, and Wei Wang, Demographic Inference via Knowledge Transfer in Cross-Domain Recommender Systems, Jin Shang, Mingxuan Sun, and Kevyn Collins-Thompson, Accurate Causal Inference on Discrete Data, HHNE: Heterogeneous Hyper-Network Embedding, Inci M Baytas, Cao Xiao, Fei Wang, Anil K. Jain, and Jiayu Zhou, Outlier Detection in Urban Traffic Flow Distributions, Youcef Djenouri, Arthur Zimek, and Marco Chiarandini, Qingquan Song, Haifeng Jin, Xiao Huang, and Xia Hu, FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation, Fei Jiang, Lei Zheng, Jin Xu, and Philip S. Yu, Evaluating Top-k Meta Path Queries on Large Heterogeneous Information Networks, Zichen Zhu, Reynold Cheng, Loc Do, Zhipeng Huang, and Haoci Zhang, Entire regularization path for sparse nonnegative interaction model, Mirai Takayanagi, Yasuo Tabei, and Hiroto Saigo, Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach, Doris Xin, Ahmed El-Kishky, De Liao, Brandon Norick, and Jiawei Han, Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention, Time Discounting Convolution for Event Sequences with Ambiguous Timestamps, Takayuki Katsuki, Takayuki Osogami, Masaki Ono, Akira Koseki, Michiharu Kudo, Masaki Makino, and Atsushi Suzuki, Maximizing the diversity of exposure in a social network, Cigdem Aslay, Antonis Matakos, Esther Galbrun, and Aristides Gionis, Clustering on Sparse Data in Non-Overlapping Feature Space with Applications to Cancer Subtyping, Tianyu Kang, Kourosh Zarringhalam, Marieke Kuijjer, John Quackenbush, and Wei Ding, Semi-Supervised Community Detection Using Structure and Size, Arjun Bakshi, Srinivasan Parthasarathy, and Kannan Srinivasan, Differentially Private Prescriptive Analytics, Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, Ramachandra Kaimal, and Svetha Venkatesh, Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, and Chengqi Zhang, Interpretable Word Embeddings For Medical Domain, Kishlay Jha, Yaqing Wang, Guangxu Xun, and Aidong Zhang, Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach, Xiaoying Ren, Linli Xu, Tianxiang Zhao, Chen Zhu, Junliang Guo, and Enhong Chen, Neural Sentence-level Sentiment Classification with Heterogeneous Supervision, Zhigang Yuan, Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, and Xing Xie, Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization, Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit, Weitong Chen, Sen Wang, Guodong Long, Lina Yao, Quan Zheng Sheng, and Xue Li, Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation, Yun He, Haochen Chen, Ziwei Zhu, and James Caverlee, A Knowledge-Enhanced Deep Recommendation Framework Incorporating GAN-based Models, Deqing Yang, Zikai Guo, Ziyi Wang, Junyang Jiang, Yanghua Xiao, and Wei Wang, Fast Tucker Factorization for Large-Scale Tensor Completion, Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing, Sein Minn, Yi Yu, Michel Desmarais, Feida Zhu, and Jill-Jenn Vie, Transfer Hawkes Processes with Content Information, Estimating Latent Relative Labeling Importances for Multi-Label Learning, Doc2Cube: Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding, Fangbo Tao, Chao Zhang, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, and Jiawei Han, Adaptive Affinity Learning for Accurate Community Detection, Fanghua Ye, Shenghui Li, Zhiwei Lin, Chuan Chen, and Zibin Zheng, Graph Pattern Mining and Learning through User-defined Relations. This workshop will provide a forum for international researchers and practitioners to share and discuss their original and interesting work on addressing new challenges and research issues in the area. for ICDM submissions, as their author By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. Accepted papers will be 2018 IEEE International Conference on Data Mining Workshops, ICDM Workshops, Singapore, Singapore, November 17-20, 2018. Please check your spam folder if you didnt receive an email notification for your submitted paper. CIKM2020 Follow. understand the author body, as well as We like to encourage state-of-the art research in the area of continual learning, model adaptation and concept drift. information that could be related to their We do not accept email submissions. Submission portal: https://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S, ICDM Workshop on Continual Learning and Adaptation for Time Evolving Data. automated data analytics, data-driven IEEE 2020, ISBN 978-1-7281-8316-9. published in the conference proceedings by the on distance-based clustering (Smith 2019), Research Paper Track; Demo Track; Tutorial Track; PhD Symposium Track; Industry Track; TKDE Poster Track; Student and Dnl Events; Keynote Session; PhD Symposium Program; Workshop Schedules; Best Papers Award; Call for Contributions. Manuscripts Applications of data mining in social Decision notifications to authors were sent out via email on 31 August. only to the PC Co-Chairs, and the author names Since 2011, ICDM has imposed There is no separate abstract submission step. All submissions will be peer reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. For diversity enhancement Posters and demos. will be used to help the organizing committee sciences, physical sciences, engineering,

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