ieee icmla acceptance rate

2022.07.31
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ieee icmla acceptance rate

2011 International Conference on Spatial Data Mining and Geographical Knowledge Services (IEEE ICSDM 2011), Fuzhou, China, June 2011. NhatHai Phan, Hoang Van Duc Thong, and Hyoseop Shin. This site uses cookies from Google to deliver its services and to analyze traffic.

ACM SIGSpatial GIS Conference Talk, Redondo Beach, Los Angeles, USA, November 2012. Korean Government Research Fellow, BK21, 2008-2010. eB Corporation Research Fellow, 2008-2010. NhatHai Phan, Xintao Wu, Dejing Dou. ECML-PKDD Conference Talk, Bristol, UK, September 2012. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2016. Authors names and affiliations should not appear in the submitted paper. Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System. Distinguishing the Effect of Time Spent at Home During COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation. learning through evolution (evolutionary algorithms), machine learning and natural language processing, multi-lingual knowledge acquisition and representation, machine learning and information retrieval, machine learning for bioinformatics and computational biology, machine learning for web navigation and mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, theories and models for plausible reasoning, o industrial and engineering applications, o medicine, bioinformatics and systems biology, o economics, business and forecasting applications. Mining Time Relaxed Gradual Moving Object Clusters. Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16), Anaheim, California, USA, December 18-20, 2016. To withdraw consent, please adjust your cookie settings in your browser. If you are already a member, simply click the first option below to access your accountand enter the email we have on file for you to begin taking advantage of your membership. Submissions for this Conference can be made by Jul 03, 2021. The trained staining model can then generate computationally H&E-stained prostate core WSRIs using previously unseen non-stained biopsy images as input. International Journal of Information Technology & Decision Making (IJITDM), 2016. Han Hu, NhatHai Phan, James Geller, Huy Vo, Bhole Manasi, Xueqi Huang, Sophie Di Lorio, Thang Dinh, Soon Ae Chun.

2018 Conference paper acceptance rate: 14%, Paper 1:Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural NetworksAman Rana, Gregory Yauney, Alarice Lowe, Pratik Shah, 12/18/18, 5:20-7:20pm, short paper and poster. medicine, biology, industry, manufacturing, security, education, virtual environments, Scalable Self-Taught Deep-embedded Learning Framework for Drug Abuse Behaviors Detection with Spatial Effects. The 2nd International Conference on Emerging Databases (EDB 2010), Jeju Korea, August 2010. We report two novel approaches for training machine learning models for the computational H&E staining and destaining of prostate core biopsy RGB images.

International Journal of Pattern Recognition and Artificial Intelligence, doi:10.1142/S0218001420520102. Proceedings of the 6th ACM International Conference on Digital Health (ACM DH'16), Montreal, Canada, April 2016. Invited to The 7th International Conference on Computational Data & Social Networks (CSoNet'18), Shanghai, China, December 2018. 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018) aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning. NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. Structural and anatomical details of prostate tissue and colors, shapes, geometries, locations of nuclei, stroma, vessels, glands and other cellular components were generated by both models with structural similarity indices of 0.68 (staining) and 0.84 (destaining). The destaining model, by learning mappings between an H&E stained WSRI and a non-stained WSRI of the same biopsy, can computationally destain previously unseen H&E-stained images. Hematoxylin and eosin stain (H&E) is one of the principal stains in histology but suffers from several shortcomings related to tissue preparation, staining protocols, slowness and human error.

(* equal contribution), FLSys: Toward an Open Ecosystem for FederatedLearning Mobile Apps. conference computer ieee 2007 acceptance rates Research on designing communication method between traffic card and client server. BDA Summer School Talk, Aussois, France, August 2012. as well as other Abstracting and Indexing (A&I) databases. IEEE International Conference on Big Data (IEEE BigData'21), December 15-18, 2021. ACM Multimedia Conference Poster Presentation, Firenze, Italy, October 2010. Information Sciences - Elsevier. [pdf], Effective Clustering of Dense and Concentrated Online Communities. NhatHai Phan, Hoang Anh Nguyen, Minh Quang Tran, and Hoang Hai Ly. The 7th International Workshop on Information Search, Integration and Personalization (ISIP 2012), Sapporo, Japan, October 2012. ICMLA 2021 : 20th IEEE International Conference on Machine Learning and Applications will take place in Pasadena, California, USA. [Oral Presentation] [Github] (Pradnya Desai is an honor undergraduate student), Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network. Tutorial on Deep Learning and Applications. [pdf], A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction. IEEE Access, July 2018. In Proceedings of the 8th International Conference on Advanced Data Mining and Applications (ADMA 2012), Nanjing, China, December 2012. with emphasis on applications as well as novel algorithms and systems. [pdf] (Selected as Best Papers), Recursive Structure Similarity: A Novel Algorithm for Graph Clustering.

[pdf] [Github] Dataset released: 5,000 drug abuse risk behavior labeled tweets has been released at: https://github.com/hu7han73/DrugAbuseLabeledTweets. APWeb Conference Talk, Busan, Korea, April 2010. Research Lab, Barcelona, Spain, May 2013. Papers submitted for review should conform to IEEE specifications. Manuscript templates can be downloaded from the IEEE website. The issue here is that many artists desire to have many fans following their activities. [link] [vimeo], An Efficient Spatio-Temporal Mining Approach to Really Know Who Travels with Whom! [pdf] [demo] [code], Moving Objects: Combining Gradual Rules and Spatio-Temporal Patterns. IEEE ICDM'17, New Orleans, USA 18-21 November 2017.

Oct 2013 - Mining Object Movement Patterns from Trajectory Data - [pdf] [Slides], Supervisors: Dr. Dino ienco, Prof. Pascal Poncelet, Prof. Maguelonne Teisseire, Prof. Osmar Zaane - University of Alberta, Prof. Arno Siebes - Utrech University, Dr. Francesco Bonchi - Yahoo! NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou.

IF = 3.532 [pdf] [Slides] [code], Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network. Shaohua Wang, NhatHai Phan, Yan Wang, and Yong Zhao. [Github], Ontology-based Interpretable Machine Learning for Textual Data. [pdf] [Github] (acceptance rate: 105 / 590), Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning. ICMLA 2018 aims to bring together researchers and practitioners to present their latest Its a 4 days event starting on Dec 13, 2021 (Monday) and will be winded up on Dec 16, 2021 (Thursday). Minh Vu, Truc D. Nguyen, NhatHai Phan, Ralucca Gera, My T. Thai. NhatHai Phan, Hoang Anh Nguyen, Minh Quang Tran, Hoang Hai Ly, and Tran Khanh Dang. Pradnya Desai, Phung Lai, NhatHai Phan, and My T. Thai. Journal of Combinatorial Optimization - Springer. achievements and innovations in the area of machine learning (ML). The 8th International Conference on Computational Data & Social Networks (CSoNet'19), Hochiminh City, Vietnam, November 2019. If you do not receive an email after following the step above AND you are a current member, you will need to select the second option below to create an account. IEEE ICHI'18, New York City, NY, USA, June 2018.

Codes representing the learned feature space of trained classifier were visualized using t-SNE embedding and were separable and distinguished between images from critically ill and non-septic patients. [pdf], Mining Fuzzy Moving Object Clusters. The 17th World Congress of Medical and Health Informatics (MedInfo'19), Lyon, France, August 2019. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. Depeng Xu, Shuhan Yuan, Xintao Wu, NhatHai Phan. Privacy in Machine Learning (PriML), NeurIPS'19 Workshop, December 8-14, 2019, Vancouver, Canada.

Pelin Ayranci, Cesar Bandera, NhatHai Phan, Ruoming Jin, Dong Li, Deric Kenne. The 28th International Joint Conference on Artificial Intelligence (IJCAI'19), August 10-16, 2019, Macao, China. The maximum length of papers is 8 pages. Won the Mathematics Contest of Khanh Hoa Province, 2002-2003. Additional membership benefits include timely notifications of conferences, symposia, and workshops in network analysis held throughout the year at various locations in the many countries represented by the INSNA membership and access to the world's leading experts on social network analysis through SOCNET (INSNAs online discussion forum) and at INSNAs signature conference, the Sunbelt conference. Aabidine, A. Sallaberry, S. Bringay, M. Fabregue, C. Lecellier, NhatHai Phan, and P. Poncelet. Limiting the Neighborhood: De-Small-World Network for Outbreak Prevention. The 12th International Asia-Pacific Web Conference (APWeb 2010), Busan, Korea, April 2010.

In the proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE-17) - 2nd HDMM Workshop (HDMM-17), San Diego, CA, USA, April 2017. [pdf] [Slides] [code] (acceptance rate: 20%), Mining Representative Movement Patterns through Compression. Phung Lai, NhatHai Phan*, Han Hu, Anuja Badeti, David Newman, and Dejing Dou. NhatHai Phan, and Hyoseop Shin. Authors should also avoid revealing their identities and/or institutions in the text, figures, links, etc. CIKM Conference Talk, Shanghai, China, December 2014. Van Duc Thong Hoang, NhatHai Phan, and Hyoseop Shin. [pdf] [Slides] [code], Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction. Classification of Ecological Data by Deep Learning. Han Hu, NhatHai Phan*, Xinyue Ye, Ruoming Jin, Kele Ding, Dejing Dou, and Huy T. Vo. Basic Algorithms Library System for Data Mining. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. Proceedings of International Workshop on Advanced Computing and Applications(ACOMP 2008), Ho Chi Minh City, Vietnam, March 2008. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. provides a leading international forum for the dissemination of original research in ML, Invited talk at Yahoo! The classifier achieves an accuracy of 89.45%. [1] ICMLA 2021 : 20th IEEE International Conference on Machine Learning and Applications. [pdf] [demo] [video] [code] (Selected as Best papers), Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System. Shaobo Liu, Frank Y. Shih, Gareth Russell, Kimberly Russell, and NhatHai Phan. [pdf], Adaptive Combination of Tag and Link-based User Similarity in Flickr. application developers from a wide range of ML related areas, and the recent emergence ICSDM Conference Talk, Fuzhou China, June 2011. [pdf] (Selected as Best papers), GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects. Invited talk at University of Oregon, Eugene, OR, USA, April 2014. The 16th International Conference on Mining Software Repositories (MSR'19), Montreal, Canada, May 26-27, 2019. Moving Object: Combining Gradual Rules and Spatio-Temporal Patterns. IEEE International Conference on Big Data (IEEE BigData'21), December 15-18, 2021. [Github] [Oral Presentation]. The authors prior work should be cited in the third person. [pdf] [code] (acceptance rate: 19%), Mining Time Relaxed Gradual Moving Object Clusters. Phung Lai, NhatHai Phan*, Abdallah Khreishah, Issa Khalil, and Xintao Wu. NhatHai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, and Dejing Dou. NhatHai Phan, Yue Wang, Xintao Wu, and Dejing Dou. Hochiminh University of Technology Scholarship for good student, 2006-2007. applications. To speed up the growing of number of fans, some of them buy spoofing fans from fan suppliers. FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective, PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning, Federated Learning for Internet of Things: Applications, Challenges, and Opportunities, Multiview Transformers for Video Recognition, CATE: Computation-aware Neural Architecture Encoding with Transformers, Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling, FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking, NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation, DeepLoRa: Learning Accurate Path Loss Model for Long Distance Links in LPWAN, Towards Position-Independent Sensing for Gesture Recognition with Wi-Fi. An Insight Analysis and Detection of Drug Abuse Risk Behavior on Twitter with Self-Taught Deep Learning. Authors of the best papers from this special session will be invited to extend their work and publish it in selected journals (SNAM, Applied Network Science, and Journal of Intelligent and Information Systems). Information about your use of this site is shared with Google. Adaptive Combination of Tag and Link-based User Similarity in Flickr. Basic Algorithms Library System for Data Mining. This special session will also serve as a common ground to build collaborations, share datasets, and inspire machine learning on graphs research in areas where there are limitations in the existing approaches. Select a membership type below to get started! Knowledge and Information Systems (KAIS), 2016. challenges. This special session at ICMLA 2021 aims to bring researchers across disciplines to share their innovative ideas on learning with graphs and leverage existing methodologies across several application domains. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. Following the

[Slides]. Invited talk at American Family Insurance, Madison, Wisconsin, USA, July 2015. This Design Justice Pedagogy Summit and overarching project is motivated by the pressing need to center and incorporate principles of ethic, Accessibility ACOMP Conference Talk, Hochiminh, Vietnam, March 2008. Han Hu, NhatHai Phan*, James Geller, Soon Ae Chun, Ruoming Jin, Kele Ding, Deric Kenne, and Dejing Dou. Han Hu, Yixing Fang, Ruoming Jin, Wei Xiong, Xiaoning Qian, Dejing Dou, NhatHai Phan*.

Computation Social Network - Springer, 2019. The 37th International Conference on Machine Learning (ICML'20), July 12 - 18, 2020. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. The 8th International Conference on Computational Data & Social Networks (CSoNet'19), Hochiminh City, Vietnam, November 2019. Graphs or networks are ubiquitous structures that appear in a multitude of complex systems like social networks, biological networks, knowledge graphs, the world wide web, transportation networks, and many more. , David Kil, Brigitte Piniewski, and Dejing Dou. All the papers will go through a double-blind peer-review process. Zhihang Hu, Turki Turki, NhatHai Phan, Jason Wang. NhatHai Phan, Dejing Dou, Hao Wang, David Kil, and Brigitte Piniewski. [pdf] [Slides] [code], All in one: Mining Multiple Movement Patterns. success of previous ICMLA conferences, the conference aims to attract researchers and (* equal contribution) [Github], A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples. Research Lab, Prof. Bruno Crmilleux - Universit de Caen, Human Behavior Modeling in Health Social Networks. (acceptance rate: 24%) [pdf], Characterizing Physical Activity in a Health Social Network.

NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire.

The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. [pdf] [demo] [code] (acceptance rate: 22%), How to Extract Relevant Knowledge from Tweets? Han Hu*, Xiaopeng Jiang*, Vijaya Datta Mayyuri, An Chen, Devu M. Shila, Adriaan Larmuseau, Ruoming Jin, Cristian Borcea, and, . Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? [GitHub], Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness. Research Lab, Barcelona, Spain 05/2013 08/2013, With the success of online social networks and microblogs such as Facebook, Flickr and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has recently attracted the interest of computer scientists, information technologists, and marketing specialists. IEEE ICDM'17, New Orleans, USA 18-21 November 2017.

of Big Data processing brings an urgent need for machine learning to address these new FedML: A Research Library and Benchmark for Federated Machine Learning, MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution, FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision, Distream: Scaling Live Video Analytics with Workload-Adaptive Distributed Edge Intelligence, Wi-Fi See It All: Generative Adversarial Network-augmented Versatile Wi-Fi Imaging, SecWIR: Securing Smart Home IoT Communications via Wi-Fi Routers with Embedded Intelligence, SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration, Deep Learning in the Era of Edge Computing: Challenges and Opportunities, DQS: A Framework for Designing Tiny Neural Networks for On-Device AI, HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking, Federated Learning: The Future of Distributed Machine Learning, AutoML Mobile: Automated ML Model Design for Every Mobile Device, NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision, The Dark Side of Operational Wi-Fi Calling Services, When Virtual Reality Meets Internet of Things in the Gym: Enabling Immersive Interactive Machine Exercises, When Mixed Reality Meets Internet of Things: Toward the Realization of Ubiquitous Mixed Reality, Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study, MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images, DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation, SharpEar: Real-Time Speech Enhancement in Noisy Environments (Poster), Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning, Helping Universities Combat Depression with Mobile Technology, BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring, HeadScan: A Wearable System for Radio-based Sensing of Head and Mouth-related Activities, AirSense: An Intelligent Home-based Sensing System for Indoor Air Quality Analytics, DoppleSleep: A Contactless Unobtrusive Sleep Sensing System Using Short-Range Doppler Radar, MyBehavior: Automatic Personalized Health Feedback from User Behavior and Preference using Smartphones, Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study, The Relationship between Clinical, Momentary, and Sensor-based Assessment of Depression, Automated Personalized Feedback for Physical Activity and Dietary Behavior Change with Mobile Phones: A Randomized Controlled Trial on Adults, An Intelligent Crowd-Worker Selection Approach for Reliable Content Labeling of Food Images, BodyBeat: A Mobile System for Sensing Non-Speech Body Sounds, Towards Accurate Non-Intrusive Recollection of Stress Levels Using Mobile Sensing and Contextual Recall, Human Daily Activity Recognition with Sparse Representation Using Wearable Sensors, Towards Practical Energy Expenditure Estimation with Mobile Phones, Motion Primitive-Based Human Activity Recognition Using a Bag-of-Features Approach, Towards Pervasive Physical Rehabilitation Using Microsoft Kinect, Beyond the Standard Clinical Rating Scales: Fine-Grained Assessment of Post-Stroke Motor Functionality Using Wearable Inertial Sensors, USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors, A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer, Sparse Representation for Motion Primitive-Based Human Activity Modeling and Recognition Using Wearable Sensors, Robust Human Activity and Sensor Location Co-Recognition via Sparse Signal Representation, Co-Recognition of Human Activity and Sensor Location via Compressed Sensing in Wearable Body Sensor Networks, Manifold Learning and Recognition of Human Activity Using Body-Area Sensors, A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors, Context-Aware Fall Detection Using A Bayesian Network, OCRdroid: A Framework to Digitize Text Using Mobile Phones, A Customizable Framework of Body Area Sensor Network for Rehabilitation.