Tất cả các máy tính đang chạy với cấu hình CPU Intel Xeon 6 lõi, với ít nhất 24GB RAM và bộ nhớ 1TB (HDD hoặc SSD). Các GPU được cài đặt bao gồm từ GTX 690 (2 x 1536 lõi), GTX Titan Black (2880 lõi), Tesla K40c (2880 lõi) đến Tesla K80 (2 x 2496 lõi). Bộ nhớ GPU dao động từ 4GB đến 12GB cho mỗi GPU. Các nút tính tính toán được kết nối với nhau bằng mạng Ethernet 1Gbps và FDR 56Gbps Infiniband. Một bộ lưu trữ RAID 15TB dùng chung được cung cấp cho mỗi nút.

Cụm này hiện được sử dụng cho các nghiên cứu khoa học khác nhau, như deep learning, computer vision, speech synthesis and recognition, v.v.

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• Huu Ton LE, Doanh NGUYEN-NGOC, Hoang Tung TRAN, Anh Tuan GIANG, Edourd AMOUROUX, Antonio-Román MUÑOZ, Fugo TAKASU. Egg pattern mimicry in avian brood parasitism assessed using local image descriptors and human-eyes. Ornithological Science, ISSN:13470558, 2022, vol. 2.

Năm 2021

• Chi Cuong Nguyen, Giang Son Tran, Van Thi Nguyen, Nghiem Thi Phuong, Jean-Christophe Burie. Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor Box. IEEE Access
  2169-3536, 2021, vol. 9, p. 154740-154751.

Năm 2020

• Giang Son Tran, Thi Phuong Nghiem, Jean-Christophe Burie. Fast Parallel blur detection on GPU. Journal of Real-Time Image Processing
  1861-8200, 2020, vol. 17, p. 903-913.
• Lê Hữu Tôn, Nguyễn Hoàng Hà. Phân loại chữ số cho các camera nhận dạng biển số giao thông tại Việt Nam. Tạp chí Khoa học và Công nghệ – Đại học Thái Nguyên
  2615-9562, 2020, vol. 225, e6, p. 451-458.
• Thi-Thanh Ha, Atsuhiro Takasu, Thanh-Chinh Nguyen, Kiem-Hieu Nguyen, Van-Nha Nguyen, Kim-Anh Nguyen, Giang-Son Tran. Supervised attention for answer selection in community question answering. IAES International Journal of Artificial Intelligence (IJ-AI)
  2252-8938, 2020, vol. 9, e2, p. 203-211.
• Larmande P, Jibril KM. Enabling a fast annotation process with the Table2Annotation tool. Genomics & Informatics 2020; 18(2): e19.
• Do Q and Larmande P. Candidate gene prioritization using graph embedding. IEEE-RIVF 2020. Ho Chi Minh, Vietnam. Proceedings Springer.

Năm 2019

• Yaw Nti-Addae, Dave Matthews, Victor Jun Ulat, Raza Syed, Guilhem Sempere, Adrien Petel, Jon Renner, Pierre Larmande, Valentin Guignon, Elizabeth Jones, Kelly Robbins. Benchmarking Database Systems for Genomic Selection Implementation. Database, 2019.
• Sempéré G, Pétel A, Rouard M, Frouin J, Hueber Y, De Bellis F, Larmande P. Gigwa v2 – Extended and improved genotype investigator. GigaScience, Volume 8, Issue 5, May 2019, giz051.
• Pierre Larmande, Huy Do, Yue Wang. OryzaGP: rice gene and protein dataset for named-entity recognition. Genomics Inform. 2019;17(2):e17. Published online June 26, 2019.
• Venice Margarette J Juanillas, Alexis Dereeper, Nicolas Beaume, Gaetan Droc, …. Pierre Larmande, Tobias Kretzschmar, Ramil P Mauleon. Rice Galaxy: an open resource for plant science. GigaScience, Volume 8, Issue 5, May 2019, giz028.
• A Ugarte, QM Dao, BH Ho, C Vigliotti, E Belda, JD Zucker, E Prifti (2018). QMSpy: an integrated modular and scalable platform for quantitative metagenomics in Pyspark. IEEE-RIVF 2019.
• G Besnard, NT Phan, BH Ho, A Dereeper, HT Nguyen, P Quénéhervé, S Bellafiore (2018), Hybrid origins of two rice-parasitic root-knot nematode species and recent expansion of Meloidogyne graminicola in Southeast Asia. Genes 2019 Feb; 10(2): 175.
• E. Daudé, C. Caron, K. Chapuis, A. Drogoul, B. Gaudou, S. Rey-Coyrehourcq, A. Saval, P. Taillandier, P. Tranourez and J.-D. Zucker. ESCAPE : Exploring by Simulation Cities Awareness on Population Evacuation. In : International workshop on Information Systems for Crisis Response and Management (ISCRAM 2019), Valencia, Spain, 2019.
• TRAN, Giang Son, NGHIEM, Thi Phuong, NGUYEN, Van Thi, et al. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. Journal of Healthcare Engineering, 2019, vol. 2019.
• WAPET, Lavoisier, TCHANA, Alain, TRAN, Giang Son, et al. Preventing the propagation of a new kind of illegitimate apps. Future Generation Computer Systems, 2019, vol. 94, p. 368-380.
• Rafael Abbeloos, Jan Erik Backlund, Martin Basterrechea Salido, Guillaume Bauchet, Omar Benites-Alfaro, Clay Birkett, Viana C Calaminos, Pierre Carceller, Guillaume Cornut, Bruno Vasques Costa, Jeremy D Edwards, Richard Finkers, Star Yanxin Gao, Mehmood Ghaffar, Philip Glaser, Valentin Guignon, Puthick Hok, Andrzej Kilian, Patrick König, Jack Elendil B Lagare, Matthias Lange, Marie-Angélique Laporte, Pierre Larmande, David LeBauer, David Lyon, David Marshall, Dave Matthews, Iain Milne, Naymesh Mistry, Nicolas Morales, Lukas Mueller, Pascal Neveu, Evangelia Papoutsoglou, Brian Pearce, Ivan Perez-Masias, Cyril Pommier, Ricardo H Ramirez-Gonzalez, Abhishek Rathore, Angel M Raque, Sebastian Raubach, Trevor Rife, Kelly Robbins, Mathieu Rouard, Chaitanya Sarma, Uwe Scholz, Peter Selby, Guilhem Sempéré, Paul Shaw, Reinhard Simon, Nahuel Soldevilla, Gordon Stephen, Qi Sun, Clarysabel Tovar, Grzegorz Uszynski, Maikel Verouden. BrAPI-an Application Programming Interface for Plant Breeding Applications. Bioinformatics, 2019.

Năm 2018

• Philippe Cubry, Christine Tranchant-Dubreuil, Anne-Céline Thuillet, Cécile Monat, Marie-Noelle Ndjiondjop, Karine Labadie, Corinne Cruaud, Stefan Engelen, Nora Scarcelli, Bénédicte Rhoné, Concetta Burgarella, Christian Dupuy, Pierre Larmande, Patrick Wincker, Olivier François, François Sabot, Yves Vigouroux. The rise and fall of African rice cultivation revealed by analysis of 246 new genomes. Current Biology, 2018, vol. 28, no 14, p. 2274-2282. e6.
• Aravind Venkatesan, Gildas Tagny Ngompe, Nordine El Hassouni, Imene Chentli, Valentin Guignon, Clement Jonquet, Manuel Ruiz, Pierre Larmande. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy. PloS one, 2018, vol. 13, no 11, p. e0198270.
• K. Chapuis, P. Taillandier, B. Gaudou, A. Drogoul and E. Daudé. A Multi-modal Urban Traffic Agent-Based Framework to Study Individual Response to Catastrophic Events. In : International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2018), AIST Tokyo Waterfront (Tokyo, Japan), Vol. 11224, T. Miller, N. Oren, Y. Sakurai, T. Savarimuthu and C. S. Tran (Eds.), Springer, Lecture Notes in Computer Science, p. 440–448, 2018.
• Giang Son Tran, Thi Phuong Nghiem and Jean-Christophe Burie, “Fast parallel blur detection on GPU.” Journal of real-time image processing, 2018.
• Nhat-Quang Doan, Hanane Azzag, Mustapha Lebbah, Hierarchical Laplacian Score for unsupervised feature selection, IEEE World Congress on Computational Intelligence (IEEE WCCI 2018), In Proceeding of International Joint Conference on Neural Networks (IJCNN 2018).
• Huy Do, Hanh Tran, Quang Khoat Than and Pierre Larmande. “Comparative study of Named-Entity Recognition methods in the agronomical domain”, 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing2018), 2018.
• Chi Cuong Nguyen, Giang Son Tran, Thi Phuong Nghiem, Nhat Quang Doan, Damien Gratadour, Jean Christophe Burie and Chi Mai Luong, “Towards Real-Time Smile Detection based on Faster Region Convolutional Neural Network”, International Conference on Multimedia Analysis and Pattern Recognition, 2018.
• Huu Ton Le, Thierry Urruty, Marie Beurton-Aimar, Thi Phuong Nghiem, Hoang Tung Tran, Romain Verset, Marie Ballere, Hien Phuong Lai and Muriel Visani, “Study of CNN Based Classification for Small Specific Datasets”, 10th Asian Conference on Intelligent Information and Database Systems, 2018.
• Kevin Chapuis, Patrick Taillandier, Misslin Renaud & Alexis Drogoul, “Gen*: a generic toolkit to generate spatially explicit synthetic populations”, International Journal of Geographical Information Science.
• Kevin Chapuis, Patrick Taillandier, Thiriot Samuel & Alexis Drogoul, “Synthetic population in social simulation: a review of methods and practices”, Journal of Artificial Societies and Social Simulation (to be published).
• Scheben A., Chan K., Mansueto L., Mauleon R., Larmande P., Alexandrov N., Wing R., McNally K., Quesneville, H., Edwards D. Progress in single access information systems for wheat and rice crop improvement. Briefing in Bioinformatics. 2018. in press.
• Jonquet C, Toulet A, Arnaud E, Aubin E, Dzalé-Yeumo E, Emonet V, Graybeal J, Laporte M-A, Musen M, Pesce V, Larmande P. AgroPortal: an ontology repository for agronomy. Comput. Electron. Agric. 2018; 144:126–143
• Tri-Cong Pham, Chi-Mai Luong, Muriel Visani, Van-Dung Hoang “Deep CNN and Data Augmentation for Skin Lesion Classification”, ACIID 2018, Quang Binh, March, Vietnam (selected as student paper award).

Năm 2017

• Dzale Yeumo E, Alaux M, Arnaud E, Aubin S, Baumann U, Buche P, Richard F, Jonquet C, Laporte MA, Larmande P, Pommier C, Protonotarios V, Reverte C, Shrestha R, Subirats I, Venkatesan A, Whan A and Quesneville H. Developing data interoperability using standards: A wheat community use case. F1000Research. 2017 ; 6:1843.
• Cohen-Boulakia S, Belhajjame K, Collin O, Chopard J, Froidevaux C, Gaignard A, Hinsen K, Larmande P, Le Bras Y, Lemoine F, Mareuil F, Ménager H, Pradal C, Blanchet C. Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities. Futur. Gener. Comput. Syst. 2017. 75 : 284-298.

Năm 2016

• H. H. Nguyen, B. Desbenoit, and M. Daniel. Realistic urban road network modelling Form GIS Data, UDMV 2016, Eurographics Workshop on Urban Data Modelling and Visualisation,  pp 9-16, Liège, Belgium, December 8th 2016, Eurographics Association Publisher, DOI: 10.2312/udmv. 20162026, ISBN 978-3-03868-013-0.
• Nguyen Thi Thu, Vuong Quang Phuong, Nguyen Hoang Ha, and Hoang Do Thanh Tung. Improving the Accuracy in Wifi-based Indoor Positioning with Kalman, In the proceeding of the national conference on some selected issues in information and communication technology, Hanoi, October 2016, p. 146-150.
• The South Green Collaborators. The South Green portal: a comprehensive resource for tropical and Mediterranean crop genomics. Curr. Plant Biol. 2016. 7-8 : 6-9.
• Sempéré G, Philippe F, Dereeper A, Ruiz M, Sarah G, Larmande P. Gigwa—Genotype investigator for genome- wide analyses. Gigascience. 2016. 5:25.
• Giang, Anh Tuan, Anthony Busson, and Marco Di Renzo, “Modeling and optimization of CSMA/CA in VANET.”, Annals of Operations Research, 1-16, 2016.
• Giang, Anh Tuan; BUSSON, A.; Lambert, A.; Gruyer, D., “Spatial capacity of IEEE 802.11p based VANET: models, simulations and experimentations,” in Vehicular Technology, IEEE Transactions on , vol.PP, no.99, pp.1-1, 2016.
• Huu Ton Le, Thierry Urruty, Syntyche Gbèhounou, François Lecellier, Jean Martinet, Christine Fernandez, “Improving retrieval framework using information gain models”, Signal, Image and Video Processing, pp. 1-8. ISSN 1863-1711, 2016.
• Van Huy Nguyen, Chi Mai Luong, Tat Thang Vu “The IOIT English ASR system for IWSLT 2016”, IWSLT 2016, Seattle, USA, 8-9 Dec. 2016.
• Giang Son Tran, Thi Phuong Nghiem,  Tuong Vinh Ho and Chi Mai Luong, “Extended Process Scheduler for Improving User Experience in Multi-core Mobile Systems,” in Proceedings of the 7th ACM International Symposium on Information and Communication Technology, SoICT’16, pp 417-424, 2016.
• Giang Son Tran, Thi Phuong Nghiem, Nhat Quang Doan, Alexis Drogoul and Chi Mai Luong, “Fast Parallel Blur Detection of Digital Images,” in Proceedings of the 12th IEEE-RIVF International Conference on Computing and Communication Technologies, RIVF’16, 2016.
• Nhat Quang Doan,  Thi Phuong Nghiem and Giang Son Tran, “Dynamic Indexing for Content-Based Image Retrieval Systems using Hierarchical and Topological Network,” in Proceedings of the 8th IEEE International Conference on Knowledge and Systems Engineering, KSE’16, 2016.
• Hammam Riza, Michael Purwoadi, Aw Ai Ti, Sharifah Mahani Aljunied Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Rapid Sun, Vichet Chea Khin Mar Soe, Khin Thandar Nwetl, Masao Utiyama, Chenchen Ding, “Introduction of Asian Language Treebank with a Survey of Asian NLP Resources”, Proceeding of Oriental COCOSDA, Oct. 26-28, Bali, Indonesia, 2016, pp118-122.
• Quoc Bao Nguyen, Tat Thang Vu, Chi Mai Luong “A study of tone modelling and deep bottleneck features for Vietnamese for LVCSR system”, Proceeding of STLU, Oct. 26-28, Bali, Indonesia, 2016, pp118-122.

Năm 2015

• Huu Ton Le, Syntyche Gbèhounou, Thierry Urruty, François Lecellier, Christine Fernandez, “Information Gain Study for Visual Vocabulary Construction”, Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, June 23-26, 2015, Shanghai, China
• Phung Trung Nghia, Luong Chi Mai, Masato Akagi, Improving the Naturalness of Concatenative Vietnamese Speech Synthesis under limited data conditions, Journal of Computer Science and Cybernetics, ISSN 1813-9663, No.1, Vol. 31, 2015, pp.1-16.
• Van Truong Nguyen, Xuan Hoai Nguyen, Chi Mai Luong, A novel combination of Negative and Positive Selection in Artificial Immune systems, VNU Journal of Science, Computer Science and Communication Engineering, No.1, Vol. 31, 2015, ISSN 0866-8612, pp.22-31.
• Van Huy Nguyen, Quoc Bao Nguyen, Tat Thang Vu and Chi Mai Luong, “The IOIT English ASR system for IWSLT 2015”, Proceeding of IWSLT 2015, Da Nang, Vietnam, 3-4 Dec. 2015., pp84-87.
• Van Huy Nguyen, Chi Mai Luong, Tat Thang Vu, “Tonal Phoneme-based model for Vietnamese LVCSR”, Proceedings of Oriental COCOSDA, Oct. 28-30, Shanghai, China, 2015, pp118-122.
• Giang Son Tran and Thi Phuong Nghiem, “Cooperative iaas resource management: Policy and simulation framework,” in Proceedings of the 7th IEEE International Conference on Knowledge and Systems Engineering, KSE’15, 2015.
• Nhat-Quang Doan, Mohammed Ghesmoune, Hanane Azzag, and Mustapha Lebbah – Growing Hierarchical Trees for Data Stream Clustering and Visualization – International Joint Conference on Neural Networks (IJCNN 2015), July 12–17, 2015, Killarney , Ireland.
• Tran, Giang Son, Alain Tchana, Daniel Hagimont, and Noel De Palma, “Cooperative Resource Management in a IaaS”, In Advanced Information Networking and Applications (AINA), 2015 IEEE 29th International Conference on, pp. 611-618. IEEE, 2015.

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ICTLab hiện đang thực hiện các chủ đề nghiên cứu sau:

• Machine Learning, Deep Learning and Data Mining
• Image and Speech Processing
• Modeling and Simulation
• Sensor Networks and Embedded Systems
• High Performance Computing
• Health Informatics and BioInformatics

Những dự án hiện tại của ICTLab:

– LittoKong (2019). Sensitization of managers to the prevention of water-related risks: modeling, simulation, and participation in the Mekong Delta. Project in collaboration with the University of Tours, the University of La Rochelle and the University of Can Tho (Vietnam). 

– HoanKiemAir (2019). Toward a tangible and interactive interface for the simulation of traffic and pollution in the Hoan Kiem district (Hanoi, Vietnam). Project in collaboration with University Thuy Loi.

– GEMMES (2019-2022). Assessment of the impact of Climate Change in Vietnam. Work Package leader of the WP4 Regio about the assessment of the local impact of climate change.

– LungCancerCare (A system to support doctors in Lung Cancer Diagnosis and Treatment): Lung cancer is one of the most serious and common types of cancer all over the world, both in number of new patients and in number of fatalities. There were a total 1.69 millions of fatalities because of lung cancer in 2015 only (source: World Health Organization). Lung cancer is the most common type of cancer for both genders in Vietnam. According to statistics from the Ministry of Health, Vietnam has 25200 new patients in 2017 and expected 30.000 new patents per year in 2020. The goal of this project is to study and develop efficient deep learning models in order to improve the accuracy and computational performance of lung cancer image analysis algorithms. Specific goals include: (1) Building an annotated image dataset for lung cancer analysis with case studies in Vietnamese hospitals; (2) Studying and proposing deep learning models for efficient detection and classification of lung tumors as benign and malignant; (3) Developing a decision support software to assist doctors in lung cancer detection.

– iMorph (Detection of geometric morphometric landmarks in 2D insect wing images): The field of morphometrics, or morphometry, is concerned with the analysis of form, which is defined by shape and size, of an object. Shape is defined as the set of geometric properties of an object that are invariant to position, orientation, and scale. In a population of specimens of interest, shape variability with size, i.e. allometry, is also considered. The main goal of morphometrics is to elucidate how shapes vary and their covariance with other variables such as diseases, environmental stresses, or development etc. Morphometrics is very important in biology because it allows quantitative descriptions of organisms, hence facilitating comparative studies by statistical analysis methods. Landmark-based geometric morphometrics uses a set of landmarks to describe shape. Landmark, a two- or three-dimensional point represented by locus coordinates, is described by a tightly defined set of rules that indicates the evolutionary significance for the organism in question. Given these defined rules, it is next necessary to identify the landmarks on each specimen. This task is normally done by an expert, however, it is time-consuming and error-prone. Therefore, in this project, we aim to automate this by processing object images and employing an image recognition & machine learning model to propose landmark candidates.

–  MiJVeh (Mitigation against Jamming Attacks in Vehicular Networks): Vehicular Ad hoc Networks (VANETs) plays a role of wireless communication technology supporting Intelligent Transportation System (ITS) in the domain of vehicles. Characteristics of the vehicular environment always create challenges. A wide range of applications and usage scenarios requires enhancements in standardized protocols and security in vehicular networks before implementing in daily life. One of the common types of attacks in VANETs is jamming. Although several detection methods have been proposed to deal with jamming attacks, such methods specified for basic safety messages in vehicular networks still remains an open topic for researching due to the variety of ITS applications and scenarios. Moreover, after detecting an attack, there is a need for a defense protocol to react to the attack. In this project, our objectives are studying and proposing mechanisms for devices in vehicular networks (vehicles installed communication units) to react to jamming attacking incidents in order to recover the communication in the networks.

–  HAGEDL (Real time Hand Gesture Recognition based on Deep Learning): Hand gesture is one of the most natural and intuitive ways people use to communicate and express emotions. Adoption of hand gestures in Human Machine Interaction (HMI) promises to provide users with comfort and ease. We introduce in this paper a vision-based recognition system for static hand gestures. Our method utilizes YOLOv3, a state of the art recognition deep neural network; therefore, it does not rely on complex image processing pipelines like many traditional systems. Experiments show that our framework is capable of processing full hd videos in real time speed with high accuracy.

– ESCAPE is an ANR project that focuses on the simulation of urban area evacuation in case of catastrophe. The simulator is based on the gamma simulation platform and is based on agent-based modeling of the evacuation process: this allows to explore individually based strategy, including individual knowledge about evacuation plan, emotion during egress and individually based mobility model with several modes. The project focuses on three case studies: chemical risk with industry explosion in the center of the city of Rouen (France), flash flood risk at the valley of Authion (France) and Hoa Binh dam break in the region of Hanoi – Red River (Vietnam).

– Gen* is an open source java library that makes it possible to generate spatially explicit and socially connected synthetic populations using any survey and GIS data. The toolkit is also available as a Gama plugin and can be used through the Kepler workflow management tool. The aim is to provide computer scientists and non programmers the access to state of the art algorithms to solve synthetic population generation, explicit localization and network generation of synthetic entities. The library is under development but already provides several algorithms for each of this three part: gospl, spll and spin. See https://github.com/ANRGenstar/genstar for further information.

– AgroLD (The Agronomic Linked Data project): Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. This data explosion in-conjunction with its heterogeneity presents a major challenge in adopting an integrative approach towards research. We are developing AgroLD, a knowledge system that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on rice species and in this way facilitating the formulation of new scientific hypotheses. The objective of this effort is to provide the community with a platform for domain specific knowledge, capable of answering complex biological questions. The current phase  covers information on genes, proteins, ontology associations, homology predictions, metabolic pathways, plant traits, and germplasm, on the Arabidopsis and rice species.

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