With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. fact-checking. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. VDS will bring together domain scientists and methods researchers (including data mining, visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in visualization in data science. SDU accepts both long (8 pages including references) and short (4 pages including references) papers. Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. Jos Miguel Hernndez-Lobato, University of CambridgeProf. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2021), (acceptance rate: 23.6%), accepted. The robust development and assured deployment of AI systems: Participants will discuss how to leverage and update common software development paradigms, e.g., DevSecOps, to incorporate relevant aspects of system-level AI assurance. Frontiers in Big Data, accepted, 2021. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. "Bridging the gap between spatial and spectral domains: A survey on graph neural networks." Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. [slides] Consult the list of programs available in the next session. This manual extraction process is usually inefficient, error-prone, and inconsistent. Characterization of fundamental limits of causal quantities using information theory. Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting. The impact of robustness assurance on other AI ethics principles: RAISA will also explore aspects related to ethical AI that overlap and interact with robustness concerns, including security, fairness, privacy, and explainability. Supplemental Workshop site:https://rl4ed.org/aaai2022/index.html. 4 pages), and position (max. Nonetheless, human-centric problems (such as activity recognition, pose estimation, affective computing, BCI, health analytics, and others) rely on information modalities with specific spatiotemporal properties. Prof. Max Welling, University of Amsterdam and Microsoft ResearchProf. In this 2nd instance of GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. Data science is the practice of deriving insights from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. All papers must be submitted in PDF format, using the AAAI-22 author kit. Specific topics of interest for the workshop include (but are not limited to) foundational and translational AI activities related to: The workshop will be a one day meeting comprising invited talks from researchers in the field, spotlight lightning talks and a poster session where contributing paper presenters can discuss their work. The submission website ishttps://cmt3.research.microsoft.com/PracticalDL2022. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. Each oral presentation will be allocated between 10-15 minutes, while the spotlight presentation will be 2 minute each. Table identification and extraction from business documents. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. Information extraction from text and semi-structured documents. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. No supplement is allowed for extended abstracts. An Invertible Graph Diffusion Model for Source Localization. Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. It further combines academia and industry in a quest for well-founded practical solutions. Some good examples include recommender systems, clustering, graph mining, Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . However, the quality of audio and video content shared online and the nature of speech and video transcripts pose many challenges to the existing natural language processing. The ability to read, understand and interpret these documents, referred to here as Document Intelligence (DI), is challenging due to their complex formats and structures, internal and external cross references deployed, quality of scans and OCR performed, and many domains of knowledge involved. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. Submission Site:https://cmt3.research.microsoft.com/SAS2022, Abdelrahman Mohamed (Facebook, abdo@fb.com), Hung-yi Lee (NTU, hungyilee@ntu.edu.tw), Shinji Watanabe (CMU, shinjiw@ieee.org), Tara Sainath (Google, tsainath@google.com), Karen Livescu (TTIC, klivescu@ttic.edu), Shang-Wen Li (Facebook, shangwel@fb.com), Ewan Dunbar (University of Toronto, ewan.dunbar@utoronto.ca) Emmanuel Dupoux (EHESS/Facebook, dpx@fb.com), Workshop URL:https://aaai-sas-2022.github.io/. Question answering on business documents. A Systematic Survey on Deep Generative Models for Graph Generation. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. Trade-Off between Privacy-Preserving and Explainable Federated Learning Federated Learning Multi-Party Computation, Federated Learning Homomorphic Encryption, Federated Learning Personalization Techniques, Federated Learning Meets Mean-Field Game Theory, Federated Learning-based Corporate Social Responsibility. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. The current research in this area is focused on extending existing ML algorithms as well as network science measures to these complex structures. BEAN: Interpretable and Efficient Learning with Biologically-Enhanced Artificial Neuronal Assembly. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. This AAAI-22 workshop on AI for Decision Optimization (AI4DO) will explore how AI can be used to significantly simplify the creation of efficient production level optimization models, thereby enabling their much wider application and resulting business values.The desired outcome of this workshop is to drive forward research and seed collaborations in this area by bringing together machine learning and decision-making from the lens of both dynamic and static optimization models. The challenge requires participants to build competitive models for diverse downstream tasks with limited labeled data and trainable parameters, by reusing self-supervised pre-trained networks. This workshop starts with acknowledging the fundamental challenges of robustness and adaptiveness in financial services modeling and explores systematic solutions to solve these underlying problems to prevent future failures. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. Natural language reasoning and inference. We will accept the extended abstracts of the relevant and recently published work too. By registering, you agree to receive emails from UdeM. Some specific topics in the context of scientific discovery and engineering design include (but not limited to): This will be a one day workshop with a number of paper presentations and poster spotlights, a poster session, several invited talks, and a panel discussion. KDD 2023 KDD '23 ​ ​ ​ August 6-10, 2023. The automated processing of unstructured data to discover knowledge from complex financial documents requires a series of techniques such as linguistic processing, semantic analysis, and knowledge representation & reasoning. There is increasing evidence that enabling AI technology has the potential to aid in the aforementioned paradigm shift. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Sign-regularized multi-task learning. Accepted submissions will have the option of being posted online on the workshop website. The workshop will be a one-day workshop, featuring speakers, panelists, and poster presenters from machine learning, biomedical informatics, natural language processing, statistics, behavior science. Each accepted paper presentation will be allocated between 15 and 20 minutes. Well also host a competition on adversarial ML along with this workshop. [Best Poster Runner-Up Award]. In the financial services industry particularly, a large amount of financial analysts work requires knowledge discovery and extraction from different data sources, such as SEC filings and industry reports, etc., before they can conduct any analysis. However, ML systems may be non-deterministic; they may re-use high-quality implementations of ML algorithms; and, the semantics of models they produce may be incomprehensible. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. The growing popularity of NAS methods demonstrates the communitys hunger for better ways of choosing or evolving network architectures that are well-matched to the problem at hand. Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. 963-971, Apr-May 2015. We will end the workshop with a panel discussion by top researchers in the field. In this workshop we would like to focus on a contrasting approach, to learn the architecture during training. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. ), Graduate (master's, specialized graduate diploma (DESS), etc. This half day workshop will focus on research into the use of AI techniques to extract knowledge from unstructured data in financial services. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. Data mining systems and platforms, and their efficiency, scalability, security and privacy. Brave new ideas to learn AI models under bias and scarcity. 2022. ACM, New York, NY, USA, 10 pages. We will end the workshop with a panel discussion by invited speakers from different fields to enlist future directions. Check the deadlines for submitting your application. Poster/short/position papers: We encourage participants to submit preliminary but interesting ideas that have not been published before as short papers. Integration of AI-based approaches with engineering prototyping and manufacturing. This workshop aims to bring together researchers from industry and academia and from different disciplines in AI and surrounding areas to explore challenges and innovations in IML. Despite gratifying achievements that have demonstrated the great potential and bright development prospect of introducing AI in education, developing and applying AI technologies to educational practice is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues. Qingzhe Li, Jessica Lin, Liang Zhao and Huzefa Rangwala. Roco Mercado, Massachusetts Institute of Technology. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia and Charu Aggarwal, "Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems. Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. It will include multiple keynote speakers, invited talks, a panel discussion, and two poster sessions for the accepted papers. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robustness of an AI system across its entire life cycle is much more complex. The goal of the inaugural HC-SSL workshop is to highlight and facilitate discussions in this area and expose the attendees to emerging potentials of SSL for human-centric representation learning, and promote responsible AI within the context of SSL. Pengtao Xie (main contact), Assistant Professor, University of California, San Diego, pengtaoxie2008@gmail.com Engineer Ln, San Diego, CA 92161 (Tel)4123206230, Marinka Zitnik, Assistant Professor, Harvard University, marinka@hms.harvard.edu 10 Shattuck Street, Boston, MA 02115 (Tel)6503086763, Byron Wallace, Assistant Professor, Northeastern University, byron@ccs.neu.edu 177 Huntington Ave, Boston, MA 02115 (Tel)4135120352, Eric P. Xing, Professor, Carnegie Mellon University, epxing@cs.cmu.edu 5000 Forbes Ave, Pittsburgh, PA 15213 (Tel)4122682559, Ramtin Hosseini, PhD Student, University of California, San Diego, rhossein@eng.ucsd.edu (Tel) 3104293825, Ethics and fairness in autonomous systems, Robust robotic design, particularly of autonomous drones and/or vehicles. Check the CFP for details Deadline: ICDM 2020 . Given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. Recently self-supervised approaches for speech/audio processing are also gaining attention. Deep Graph Learning for Circuit Deobfuscation. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji and Charu Aggarwal. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. Checklist for Revising a SIGKDD Data Mining Paper: in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. ETA (expected time-of-arrival) prediction. There will be about 60~85 people to participate, including the program committee, invited speakers, panelists, authors of accepted papers, winners of the competition and other interested people. Disentangled Spatiotemporal Graph Generative Model. Feature Constrained Multi-Task Learnings for Event Forecasting in Social Media." ), Learning with algebraic or combinatorial structure, Link analysis/prediction, node classification, graph classification, clustering for complex graph structures, Theoretical analysis of graph algorithms or models, Optimization methods for graphs/manifolds, Probabilistic and graphical models for structured data, Unsupervised graph/manifold embedding methods. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Deep Generative Model for Periodic Graphs. We also welcome submissions that are currently under consideration in such archival venues. text, images, and videos). Adversarial attacking deep learning systems, Robust architectures against adversarial attacks, Hardware implementation and on-device deployment, Benchmark for evaluating model robustness, New methodologies and architectures for efficient and robust deep learning, December 3, 2021 Acceptance Notification, Applications of privacy-preserving AI systems, Differential privacy: theory and applications, Distributed privacy-preserving algorithms, Privacy preserving optimization and machine learning, Privacy preserving test cases and benchmarks. [Best Paper Candidate], Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao. Position papers are welcome. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. Big Data 2022 December 13-16, 2022. Submission Guidelines You signed in with another tab or window. The audience of this workshop will be researchers and students from a wide array of disciplines including, but not limited to, statistics, computer science, economics, public policy, psychology, management, and decision science, who work at the intersection of causal inference, machine learning, and behavior science. The research contributions may discuss technical challenges of reading and interpreting business documents and present research results. The 9th International Conference on Learning Representations (ICLR 2021), (acceptance rate: 28.7%), accepted. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. The trustworthy issues of clinical AI methods were not discussed. We expect 50~75 participants and potentially more according to our past experiences. Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data. The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. Generative Deep Learning for Macromolecular Structure and Dynamics, Current Opinion in Structural Biology, (impact factor: 7.108), Section on Theory and Simulation/Computational Methods 67: 170-177, 2021 accepted. Please use ACM Conference templates (two column format). Submission site:https://easychair.org/conferences/?conf=kdf22, Chair:Xiaomo Liu (J.P. Morgan Chase AI Research, xiaomo.liu@jpmchase.com), Zhiqiang Ma (J.P. Morgan Chase AI Research), Armineh Nourbakhsh (J.P. Morgan Chase AI Research), Sameena Shah (J.P. Morgan Chase AI Research), Gerard de Melo (Hasso Plattner Institute), Le Song (Mohamed bin Zayed University of Artificial Intelligence), Workshop URL:https://aaai-kdf.github.io/kdf2022/. Xiaojie Guo, Liang Zhao, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao. Submit to: Papers are required to submit to:https://easychair.org/conferences/?conf=dlg22. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. 1799-1808. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. The workshop will focus on the application of AI to problems in cyber-security. A tag already exists with the provided branch name. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. In this workshop, we aim to address the trustworthy issues of clinical AI solutions. Microsoft's Conference Management Toolkit is a hosted academic conference management system. Information theory has demonstrated great potential to solve the above challenges. Submission site:https://cmt3.research.microsoft.com/DSTC102022, Koichiro Yoshino,Address: 2-2-2, Seika, Sohraku, Kyoto, 6190288, JapanAffiliation: RIKENPhone: +81-774-95-1360Email: koichiro.yoshino@riken.jp, Yun-Nung (Vivian) ChenAddress: No. The workshop is a full day. We send a public call and we assume the workshop will be of interest to many AAAI main conference audiences; we expect 50 participants. Onn Shehory, Bar Ilan University (onn.shehory@biu.ac.il), Eitan Farchi, IBM Research Haifa (farchi@il.ibm.com), Guy Barash, Western Digital (Guy.Barash@wdc.com), Supplemental workshop site:https://sites.google.com/view/edsmls-2022/home. Submitted technical papers can be up to 4 pages long (excluding references and appendices). We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. Prediction-time Efficient Classification Using Feature Computational Dependencies. Papers will be submitted electronically using Easychair. The official dates for submitting an application are detailed below, but see the exact deadline posted on the Description Page for the program of study. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Universit de MontralOffice of Admissions and RecruitmentC. We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. to protect data owner privacy in FL. The extraction, representation, and sharing of health data, patient preference elicitation, personalization of generic therapy plans, adaptation to care environments and available health expertise, and making medical information accessible to patients are some of the relevant problems in need of AI-based solutions. Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. Junxiang Wang, Hongyi Li, Liang Zhao. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^.
Lincoln Children's Zoo Membership, Articles K
Lincoln Children's Zoo Membership, Articles K