kdd 2022 deadline

1145/3394486.3403221. Deep Generation of Heterogeneous Networks. Submissions of technical papers can be up to 7 pages excluding references and appendices. This manual extraction process is usually inefficient, error-prone, and inconsistent. GraphGT: Machine Learning Datasets for Deep Graph Generation and Transformation. Papers must be in PDF format, in English, and formatted according to the AAAI template. Share. If it turns out that the architecture is not appropriate for the task, the user must repeatedly adjust the architecture and retrain the network until an acceptable architecture has been obtained. Information theory has demonstrated great potential to solve the above challenges. The topics for AIBSD 2022 include, but are not limited to: This one-day workshop will include invited talks from keynote speakers, and oral/spotlight presentations of the accepted papers. Liang Zhao, Feng Chen, and Yanfang Ye. We welcome the submissions in the following two formats: The submissions should adhere to theAAAI paper guidelines. In addition, any other work on dialog research is welcome to the general technical track. These research trends inform the need to explore the intersection of AI with behavioral science and causal inference, and how they can come together for applications in the social and health sciences. In other words, many existing FL solutions are still exposed to various security and privacy threats. The submission website ishttps://cmt3.research.microsoft.com/TAIH2022. ETA (expected time-of-arrival) prediction. Previously published work (or under-review) is acceptable. We invite thought-provoking submissions on a range of topics in fields including, but not limited to, the following areas: The full-day workshop will start with a keynote talk, followed by an invited talk and contributed paper presentations in the morning. (Depending on the volume of submissions, we may be able to accommodate only a subset of them.). Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. We encourage all the teams who participated in the challenge to join the workshop. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Junxiang Wang, Zheng Chai, Yue Cheng, and Liang Zhao. Important Dates. This workshop aims to discuss important topics about adversarial ML to deepen our understanding of ML models in adversarial environments and build reliable ML systems in the real world. Submit to: Submissions should be made via EasyChair athttps://easychair.org/conferences/?conf=it4dl, Jose C. Principe (University of Florida, principe@cnel.ufl.edu), Robert Jenssen (UiT The Arctic University of Norway, robert.jenssen@uit.no), Badong Chen (Xian Jiaotong University, chenbd@mail.xjtu.edu.cn), Shujian Yu (UiT The Arctic University of Norway, yusj9011@gmail.com), Supplemental workshop site:https://www.it4dl.org/. The deadline for the submissions is July 31st, 2022 11.59 PM (Anywhere on Earth time). Motif-guided Heterogeneous Graph Deep Generation. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting. Dialog systems and related technologies, including natural language processing, audio and speech processing, and vision information processing. We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. 5, pp. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted. We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures such as hypergraphs, multilayer networks, multi-relational graphs, heterogeneous information networks, multi-modal graphs, signed networks, bipartite networks, temporal/dynamic graphs, etc. 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. Connor Coley, Massachusetts Institute of TechnologyProf. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. How can the financial services industry balance the regulatory compliance and model governance pressures with adaptive models, Methods to combine scientific knowledge and data to build accurate predictive models, Adaptive experiment design under resource constraints, Learning cheap surrogate models to accelerate simulations, Learning effective representations for structured data, Uncertainty quantification and reasoning tools for decision-making, Explainable AI for both prediction and decision-making, Integrating AI tools into existing workflows, Challenges in applying and deployment of AI in the real-world. We also welcome submissions that are currently under consideration in such archival venues. We will accept both original papers up to 8 pages in length (including references) as well as position papers and papers covering work in progress up to 4 pages in length (not including references).Submission will be through Easychair at the AAAI-22 Workshop AI4DO submission site, Professor Bistra Dilkina (dilkina@usc.edu), USC and Dr. Segev Wasserkrug, (segevw@il.ibm.com), IBM Research, Prof. Andrea Lodi (andrea.lodi@cornell.edu), Jacobs Technion-Cornell Institute IIT and Dr. Dharmashankar Subrmanian (dharmash@us.ibm.com), IBM Research. Submission link:https://easychair.org/cfp/raisa-2022, William Streilein, MIT Lincoln Laboratory, 244 Wood St., Lexington, MA, 02420, (781) 981-7200, wws@ll.mit.edu, Olivia Brown (MIT Lincoln Laboratory, Olivia.Brown@ll.mit.edu), Rajmonda Caceres (MIT Lincoln Laboratory, Rajmonda.Caceres@ll.mit.edu), Tina Eliassi-Rad (Northeastern University, teliassirad@northeastern.edu), David Martinez (MIT Lincoln Laboratory, dmartinez@ll.mit.edu), Sanjeev Mohindra (MIT Lincoln Laboratory, smohindra@ll.mit.edu), Elham Tabassi (National Institute of Standards and Technology, elham.tabassi@nist.gov), Workshop URL:https://sites.google.com/view/raisa-2022/. The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. Some examples of the success of information theory in causal inference are: the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery; the use of the information bottleneck principle with applications in the generalization of machine learning models; analyzing causal structures of deep neural networks with information theory; among others. Aug 14-18. By entering your email, you consent to receive communications from UdeM. robust and interpretable natural language processing for healthcare. ), responsible development of human-centric SSL (e.g., safety, limitations, societal impacts, and unintended consequences), ethical and legal implications of using SSL on human-centric data, implications of SSL on robustness and fairness, implications of SSL on privacy and security, interpretability and explainability of human-centric SSL frameworks, if your work broadly addresses the use of unlabeled human-centric data with unsupervised or semi-supervised learning, if your work focuses on architectures and frameworks for SSL for sensory data beyond CV and NLP (but not necessarily human-centric data). 19-25, 2016. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. Inspired by the question, there is a trend in the machine learning community to adopt self-supervised approaches to pre-train deep networks. The workshop combines several disciplines, including ML, software engineering (with emphasis on quality), security, and game theory. Dr. Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. Accepted submissions will be notified latest by August 7th, 2022. It will start with a 60-minute mini-tutorial covering the basics of RL in games, and will include 2-4 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel. Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. Three categories of contributions are sought: full-research papers up to 8 pages; short papers up to 4 pages; and posters and demos up to 2 pages. "A Uniform Representation for Trajectory Learning Tasks", 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2017), short paper, DOI=10.1145/3139958.3140017, Redondo Beach, CA, USA, Nov 2017. The workshop will be a one-day meeting and will include a number of technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a tutorial talk, and will conclude with a panel discussion. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. ACM RecSys 2022 will be held in Seattle, USA, from September 18 - 23, 2022. Additionally, adversaries continue to develop new attacks. All the workshop chairs, most of the Committees, and the authors of the accepted papers will attend the workshop also. The main objective of the workshop is to bring researchers together to discuss ideas, preliminary results, and ongoing research in the field of reinforcement in games. Application fees are not refundable. We invite submissions of technical papers up to 7 pages excluding references and appendices. Recent years have witnessed growing efforts from the AI research community devoted to advancing our education and promising results have been obtained in solving various critical problems in education. This workshop aims to provide a premier interdisciplinary forum for researchers in different communities to discuss the most recent trends, innovations, applications, and challenges of optimal transport and structured data modeling. NOTE: Mandatory abstract deadline on Oct 13, 2022. The fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. Welcome to PAKDD2022. Online and Distributed Robust Regressions with Extremely Noisy Labels. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. How can we develop solid technical visions and new paradigms about AI Safety? Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Submissions of technical papers can be up to 7 pages excluding references and appendices. 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. However, theoreticians and practitioners of AI and Safety are confronted with different levels of safety, different ethical standards and values, and different degrees of liability, that force them to examine a multitude of trade-offs and alternative solutions. What safety engineering considerations are required to develop safe human-machine interaction? The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. Second, psychological experiments in laboratories and in the field, in partnership with technology companies (e.g., using apps), to measure behavioral outcomes are being increasingly used for informing intervention design. This date takes priority over those shown below and could be extended for some programs. The academic session will focus on most recent research developments on GNNs in various application domains. "How events unfold: spatiotemporal mining in social media." KDD 2022. 10 (2014): e110206. Please note that the KDD Cup workshop will have no proceedings and the authors retain full rights to submit or post the paper at any other venue. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187. Liming Zhang, Liang Zhao, Dieter Pfoser, Shan Qin and Chen Ling. The reproducibility papers include a clarification phase: Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. 17th International Workshop on Mining and Learning with Graphs. To push forward the research on acronym understanding in scientific text, we propose two shared tasks on acronym extraction (i.e., recognizing acronyms and phrases in text) and disambiguation (i.e., finding the correct expansion for an ambiguous acronym). 2999-3006, New Orleans, US, Feb 2018. The post-lunch session will feature a second keynote talk, two invited talks. . Submissions that are already accepted or under review for another conference or already accepted for a journal are not accepted. The mission of the TRASE workshop is to bring together researchers from multiple engineering disciplines, including Computer Science, and Computer, Mechanical, Electrical, and Systems Engineering, who focus their energies in understanding both specific TRASE subproblems, such as perception, planning, and control, as well as robust and reliable end-to-end integration of autonomy. Novel algorithms and theories to improve model robustness. Each full paper will be reviewed by three PC members, while extended abstracts will not be reviewed. Representation learning, distributed representations learning and encoding in natural language processing for financial documents; Synthetic or genuine financial datasets and benchmarking baseline models; Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets; Search and question answering systems designed for financial corpora; Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents; Knowledge alignment and integration from heterogeneous data; Using multi-modal data in knowledge discovery for financial applications; Data acquisition, augmentation, feature engineering, and analysis for investment and risk management; Automatic data extraction from financial fillings and quality verification; Event discovery from alternative data and impact on organization equity price; AI systems for relationship extraction and risk assessment from legal documents; Accounting for Black-Swan events in knowledge discovery methods. ADMM for Efficient Deep Learning with Global Convergence. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. Liang Zhao. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. 4498-4505, New Orleans, US, Feb 2018. Other submissions will be evaluated by a committee based on their novelty and insights. This workshop brings together researchers from diverse backgrounds with different perspectives to discuss languages, formalisms and representations that are appropriate for combining learning and reasoning. . Submission site:https://openreview.net/group?id=AAAI.org/2022/Workshop/ADAM, Aarti Singh (Carnegie Mellon University), Baskar Ganapathysubramanian (ISU), Chinmay Hegde (New York University; contact: chinmay.h@nyu.edu), Mark Fuge (University of Maryland), Olga Wodo (University of Buffalo), Payel Das (IBM), Soumalya Sarkar (Raytheon), Workshop website:https://adam-aaai2022.github.io/. Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. ), Programs also suitable for students not fluent in French, Information and Communication Technologies, Graduate (master's, specialized graduate diploma (DESS), microprogram): February 1, Graduate (master's, specialized graduate diploma (DESS), microprogram): September 1. Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. 1059-1072, May 1 2017. This cookie is set by GDPR Cookie Consent plugin. We send a public call and we assume the workshop will be of interest to many AAAI main conference audiences; we expect 50 participants. The financial services industry relies heavily on AI and Machine Learning solutions across all business functions and services. 1-39, November 2016. Deep Graph Transformation for Attributed, Directed, and Signed Networks. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. Han Wang, Hossein Sayadi, Avesta Sasan, Houman Homayoun, Liang Zhao, Tinoosh Mohsenin, Setareh Rafatirad. However, these real-world applications typically translate to problem domains where it is extremely challenging to even obtain raw data, let alone annotated data. 3, pp. 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. The objective of this workshop is to discuss the winning submissions of the Submissions to the Amazon KDD Cup 2022 issingle-blind (author names and affiliations should be listed). We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. Martin Michalowski, PhD, FAMIA (Co-chair), University of Minnesota; Arash Shaban-Nejad, PhD, MPH (Co-chair), The University of Tennessee Health Science Center Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics; Simone Bianco, PhD (Co-chair), IBM Almaden Research Center; Szymon Wilk, PhD, Poznan University of Technology; David L. Buckeridge, MD, PhD, McGill University; John S. Brownstein, PhD, Boston Childrens Hospital, Workshop URL:http://w3phiai2022.w3phi.com/. The advances in web science and technology for data management, integration, mining, classification, filtering, and visualization has given rise to a variety of applications representing real-time data on epidemics. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is Apr 11-14, 2022. ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. Government day with NSF, NIH, DARPA, NIST, and IARPA, Local industries in the DC Metro Area, including the Amazons second headquarter, New initiatives at KDD 2022: undergraduate research and poster session, Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel, Workshops and hands-on tutorials on emerging topics. The AAAI-22 workshop program includes 39 workshops covering a [] In addition, authors can provide an optional one page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. The theme of the hack-a-thon will be decided before submission is closed and will be focused around finding creative solutions to novel problems in health. The first achievements in playing these games at super-human level were attained with methods that relied on and exploited domain expertise that was designed manually (e.g. 25-50 attendees including invited speakers and accepted papers. Dynamic Tracking and Relative Ranking of Airport Threats from News and Social Media. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. 105, no. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. In spite of substantial research focusing on discovery from news, web, and social media data, its applications to datasets in professional settings such as financial filings and government reports, still present huge challenges. 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. arXiv preprint arXiv:2207.09542 (2022). Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. As a result, many AI/ML systems faced serious performance challenges and failures. Integration of Deep learning and Constraint programming. Adaptive Kernel Graph Neural Network. Detailed information could be found on the website of the workshop. KDD is the premier Data Science conference. Accepted submissions will have the option of being posted online on the workshop website. These abrupt changes impacted the environmental assumptions used by AI/ML systems and their corresponding input data patterns. San Francisco, USA . Xiaojie Guo, Lingfei Wu, Liang Zhao. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. Data Mining Conference Acceptance Rate. AI System Robustness: participants will consider techniques for detecting and mitigating vulnerabilities at each of the processing stages of an AI system, including: the input stage of sensing and measurement, the data conditioning stage, during training and application of machine learning algorithms, the human-machine teaming stage, and during operational use. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. No supplement is allowed for extended abstracts. Information extraction from text and semi-structured documents. We are excited to continue promoting innovation in self-supervision for the speech/audio processing fields and inspiring the fields to contribute to the general machine learning community. We invite paper submission with a focus that aligns with the goals of this workshop. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. There is a need for the research community to develop novel solutions for these practical issues. 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. Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. Yuyang Gao, Tong Sun, Guangji Bai, Siyi Gu, Sungsoo Hong, and Liang Zhao. https://doi.org/10. The accepted papers will be allocated either a contributed talk or a poster presentation. Attendance is open to all registered participants. text, images, and videos). 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. Integration of logical inference in training deep models. And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. RAISAs systems-level perspective will be emphasized via three main thrusts: AI threat modeling, AI system robustness, explainable AI, system lifecycle attacks, system verification and validation, robustness benchmarks and standards, robustness to black-box and white-box adversarial attacks, defenses against training, operational and inversion attacks, AI system confidentiality, integrity, and availability, AI system fairness and bias. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. Ourprevious workshop at AAAI-21generated significant interest from the community. [Best Paper Award]. job seekers, employers, recruiters and job agents. The post-lunch session will feature one long talk, two short talks, and a poster session. Submissions introducing interesting experimental phenomena and open problems of optimal transport and structured data modeling are welcome as well. Deep Geometric Neural Networks for Spatial Interpolation. By clicking Accept All, you consent to the use of ALL the cookies. . ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. Additional advantages are possible, including decreased computational resources to solve a problem, reduced time for the network to make predictions, reduced requirements for training set size, and avoiding catastrophic forgetting. 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. Deep Generative Model for Periodic Graphs. Taking the pulse of COVID-19: a spatiotemporal perspective. Sign-regularized multi-task learning. [Best Paper Award Shortlist]. This thread already has a best answer. The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. Options include pruning a trained network or training many networks automatically. A tag already exists with the provided branch name. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. search, ranking, recommendation, and personalization. Submission instructions will be available at the workshop web page. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. At AAAI 2021, we successfully organized this workshop (https://taih20.github.io/). Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), industrial track, pp. If the admission deadline for international applicants is past, we suggest that you choose another session to begin your studies. "Multi-Task Learning for Spatio-Temporal Event Forecasting." Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Integration of declarative and procedural domain knowledge in learning. Malicious attacks for ML models to identify their vulnerability in black-box/real-world scenarios. Identification of key challenges and opportunities for future research. IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted. Track 2 focuses on the state of the art advances in the computational jobs marketplace. Computer Science and Engineering, INESC-ID, IST Ulisboa, Lisbon, Portugal currently at Sorbonne University, Paris, France silvia.tulli@gaips.inesc-id.pt), Prashan Madumal (Science and Information Systems, University of Melbourne, Parkville, Australia pmathugama@student.unimelb.edu.au), Mark T. Keane (School of Computer Science, University College Dublin, Dublin, Ireland mark.keane@ucd.ie), David W. Aha (Navy Center for Applied Research in AI, Naval Research Laboratory, Washington, DC, USA david.aha@nrl.navy.mil), Adam Johns (Drexel University, Philadelphia, PA USA), Tathagata Chakraborti (IBM Research AI, Cambridge, MA USA), Kim Baraka (VU University Amsterdam, Netherlands), Isaac Lage (Harvard University, Cambridge, MA USA), David Martens (University of Antwerp, Belgium), Mohamed Chetouani (Sorbonne Universit, Paris, France), Peter Flach (University of Bristol, United Kingdom), Kacper Sokol (University of Bristol, United Kingdom), Ofra Amir (Technion, Haifa, Israel), Dimitrios Letsios (Kings College London, London, United Kingdom), Supplemental workshop site:https://sites.google.com/view/eaai-ws-2022/topic.

Mdhs Child Care Upload Documents, Martin County Jail Commissary, Articles K