AI_India_compilation

Special Interest Group in Artificial Intelligence (SIGAI)

-1-

Preface The special interest group in AI, of the Computer Society of India, is happy to present you this compilation of AI activities in India. The compilation is aimed at furthering interaction amongst AI researchers and to spread awareness of AI work in India.We hope that you will find this compilation useful. At the outset, let me mention that this compilation is an ongoing process, and hence surely incomplete at any point of time! We will be maintaining an updated version of this on the SIGAI website at http://sigai.cdacmumbai.in; do send in your inputs as and when you find anything relevant. If you would like your group to be included, please fill in the form on the last page. If you would like to have someone you know included, please let us know using the referral sheet at the end or request them to fill in the form at the end of this booklet and mail it to us. We would like to expand the reach to cover all the AI groups, however small, in this compilation. Do spread the word around. We will keep updating the compilation regularly based on such inputs. This compilation is a process we started with the first national event of SIGAI - the  National Workshop on AI, held in CDAC Mumbai during July 2006. We have since then revised the template incorporating additional information, and have also tried to reach out to more institutions. institutions. The compilation owes much to the conveners of SIGAI - Prof PVS Rao and Dr S Ramani, and also to the steering committee committee of IJCAI-07 for the vision, guidance guidance and support. Wish all of you a prosperous and AI enriched new year! M Sasikumar  Secretary, SIGAI January, 2007

-2-

Preface The special interest group in AI, of the Computer Society of India, is happy to present you this compilation of AI activities in India. The compilation is aimed at furthering interaction amongst AI researchers and to spread awareness of AI work in India.We hope that you will find this compilation useful. At the outset, let me mention that this compilation is an ongoing process, and hence surely incomplete at any point of time! We will be maintaining an updated version of this on the SIGAI website at http://sigai.cdacmumbai.in; do send in your inputs as and when you find anything relevant. If you would like your group to be included, please fill in the form on the last page. If you would like to have someone you know included, please let us know using the referral sheet at the end or request them to fill in the form at the end of this booklet and mail it to us. We would like to expand the reach to cover all the AI groups, however small, in this compilation. Do spread the word around. We will keep updating the compilation regularly based on such inputs. This compilation is a process we started with the first national event of SIGAI - the  National Workshop on AI, held in CDAC Mumbai during July 2006. We have since then revised the template incorporating additional information, and have also tried to reach out to more institutions. institutions. The compilation owes much to the conveners of SIGAI - Prof PVS Rao and Dr S Ramani, and also to the steering committee committee of IJCAI-07 for the vision, guidance guidance and support. Wish all of you a prosperous and AI enriched new year! M Sasikumar  Secretary, SIGAI January, 2007

-2-

-3-

Table of Contents Bhabha Atomic Research Centre, Centre, Mumbai..................................................... Mumbai........................................................................................ ................................... 6 Division Division of Remote Handling Handling & Robotics (DRHR).................................................................... (DRHR).................................................................... .. 6 Autonomous Robotics Section (ARS)......................................................................................6 Centre for Development Development of Advanced Computing............................................................................. Computing............................................................................. 7 CDAC Mumbai..............................................................................................................................7 Knowledge Based Computer Systems Divisi Division........................................................................ on........................................................................ 7 HP Labs India, Bangalore................................................................................................. ................. 9 Language Technology and Applications Applications ....................................................................................... 9 Indian Institute Institute of Management Calcutta, Kolkata........................................................................... 11 Management Management Information Information Systems.............................................................................................. Systems.............................................................................................. 11 Indian Institute of Technology Technology Madras, Madras, Chennai........................................................ Chennai............................................................................. ..................... 12 Department of Biotechnology......................................................................................................12 Computer Science and Engineering.............................................................................................12 Interactive Intelli Intelligence gence Laboratory Laboratory ............................................................. ........................... 12 Indian Institute of Technology, Kanpur........................................................................................ ... 14 Department of Computer Science and Engineering Engineering ............................................... .....................14 ..................... 14 Department of Electrical Engineeri Engineering ng ......................................................................................... .................................................... ..................................... 14 Department of Mechanical Mechanical Engineering Engineering .................................................................... ..................15 Kanpur Genetic Algorithm Algorithmss Laboratory (KanGAL)............................................................... (KanGAL)............................................................... 15 Indian Institute of Technology, Kharagpur.................................................................................... .. 17 Computer Science & Engineering.............................................................................. Engineering.............................................................................. ..................17 Department of Civil Civil Engineering................................................................................................. Engineering................................................................................................. 17 Soft Computing in Civil Civil Engineering Engineering............................................... ............................................... ....................................... 17 Indian Institute of Technology, Guwahati........................................................................................21 Department of Computer Science and Engineering Engineering ............................................... .....................21 ..................... 21 Electronics and Communication Communication Engineering Engineering (ECE)................................................................... (ECE)............................................ ....................... 22 Indian Institute of Technology-Bombay Technology-Bombay,, Mumbai............................................................................ Mumbai............................................................................ 24 Department of Computer Science and Engineering..................................................................... Engineering..................................................................... 24  Natural Language Processing with with focus on Indian Language Language Computing............................ 24 School of Biosciences Biosciences and Bioengineering Bioengineering.................................................. .................................................. ................................ 25 Intelligent Intelligent Systems............................................................................................ Systems............................................................................................ ...................... 25 International Institute Institute of Information Technology, Hyderabad........................................................ Hyderabad........................................................ 27 Centre for Data Engineering........................................................................................................27 Centre for Visual Visual Information Information Technology (CVIT)............................................................ .........27 Language Technologies Research Centre....................................................................................28 Search and Information Information Extraction Lab.......................................................................... Lab.......................................................................... ........ 30 Speech Processing Group.................................. ....................................................... ..................................................................... .............. 31 Robotics Research Center ...................................................... .......................................................................................................... .................................................... 33 Indian Statistical Statistical Institute, Kolkata........................................................... Kolkata........................................................... ....................................... 35 Computer Vision and and Pattern Recognition (CVPR) Unit............................................................ 35 Indian Language, Script and Document Document Processing group................................. .................... 35 Jadavpur University University,, Kolkata............................................................................... ............................ 36 -4-

Computer Science & Engineering Department ............................................... ............................36  National Chemical Laboratory, Pune................................ ................................................................37 Chemical Engineering and Process Development Division.......................................................... 37 Artificial Intelligence Systems Group (AISG)........................................................................ 37 Tata Institute of Fundamental Research ..........................................................................................40 School of Technology and Computer Science ............................................................................40 Spoken Language Processing................................................................................................. 40 Tata Consultancy Services Limited................................................................................................ .. 42 TCS Mumbai..................................................................................................... .......................... 42 Cognitive Systems Research Laboratory (Applied Technology Applications Group)............ 42 TCS Delhi ................................................... ................................................................................ 43 iLab, Applied Artificial Intelligence........................................................................................ 43 Technology Innovation Lab.............................................................................. ...................... 44 Area-wise Index of Research groups................................................................................................46 Information Template for the Compilation...................................................................................... 47 Referral Sheet...................................................................................................................................48

-5-

Bhabha Atomic Research Centre, Mumbai

Division of Remote Handling & Robotics (DRHR) Autonomous Robotics Section (ARS) Information provided by : Prabir K Pal ([email protected]) Major focus areas of the group: Applications of AI in Robotics: Gait studies of legged robots, Motion planning of Robot manipulators, Mobile robot navigation and mapping, Neural networks for robot learning of obstacle avoidance and goal following. Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Laser based Mobile Robot Navigation & Mapping: We wish to develop software for map building and localization over a wide (40m x 40m) indoor area with the help of range data from Laser Range Finder. We have in the past developed such software for use in smaller areas. Our  effort is directed to make the software highly reliable for use in industrial setup as a pose sensor. 2. Mobile robot navigation in outdoor environment with 3D laser range finder and panoramic camera. At present, we are busy building the robot. The task of navigation in outdoor  environment is very complex because of the fact that the planes of the sensors keep changing continuously. We are yet to start solving these problems. But we have deep interest in this area as it is of utmost importance in many real applications of deployment of mobile robots. 3. Force sensing and control in robot manipulation: This is again a very important area for  autonomous handling of objects by robots. We plan to buy a robot arm, integrate it with Force/Torque sensor and gripper and write controller programs for force sensing and control. This is yet to start. Most recent 3-5 publications: 1. Pal, P.K. and Kar, A., "Sonar-based mobile robot navigation through supervised learning on a neural net", Autonomous Robots 3, pp. 355-374, 1996. 2. Pal P.K. and Kar D.C., "Gait Optimization through Search", The International Journal of  Robotics Research, Vol.19, No.4, April 2000, pp. 394-408. 3. "Sensor based mobile robot navigation through curvature activation and context switching", Asim Kar and P.K.Pal, National Conference on Advanced Manufacturing & Robotics, Durgapur, Jan 15-16, 2004. Some significant past projects executed: 1. Development of a motion planner for an in-house 4-axes robot (1991-93) 2. Robot learning of obstacle avoidance and goal following behavior using recurrent neural net (1994-96) 3. Development of a mobile robot and its navigation software for simultaneous localization and mapping (SLAM) (2002-2005)

-6-

Centre for Development of Advanced Computing

CDAC Mumbai Knowledge Based Computer Systems Division Information provided by : Dr M Sasikumar ([email protected]) Other senior members: Jayprasad Hegde ([email protected]); Kavitha Mohanraj ([email protected]); Jojumon K ([email protected]); Ananthakrishnan R  ([email protected]) Website (for more information): http://www.cdacmumbai.in/research/kbcs Major focus areas of the group: Data Mining, Expert Systems, Natural Language Processing, Soft Computing, and Planning and Scheduling. Brief description of at most three projects in progress:  Please include any website for more details for each project  1. MaTra is an English to Hindi machine translation aimed at producing indicative translations for  relatively open domains such as Web documents. 2. Setu is a Cross Language Information Retrieval from English to Hindi built using MaTra for  translation and a traditional search engine for web search. Setu is, currently, part of the DGF supported ICT R&T centre project. 3. Mulyaankan is a data mining application aimed at discrepancy detection in valuation of import items, developed for the Department of Valuation. Most recent 3-5 publications: 1. Some Issues in Automatic Evaluation of English-Hindi MT: More Blues for BLEU, R. Ananthakrishnan, Pushpak Bhattacharyya, M. Sasikumar and Ritesh M. Shah,, ICON 2007, Hyderabad, India, Jan, 2007 2. MaTra: a Practical Approach to Fully-Automatic Indicative English-Hindi Machine Translation, Ananthakrishnan R, Kavitha M, Jayprasad Hegde, Chandra Shekhar, Ritesh Shah, Sawani Bade and Sasikumar M, Symposium on Modeling and Shallow Parsing of Indian Languages (MSPIL'06), IIT Bombay, 2006. 3. Software Localization: Some Issues and Challenges, M Sasikumar and Jayprasad J. Hegde, Conference on Sharing Capability in Localization and Human Language Technologies (SCALLA), 2004. 4. A Lightweight Stemmer for Hindi, Ananthakrishnan R and Durgesh Rao, Workshop on Computational Linguistics for South Asian Languages at the 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL'03), 2003. 5. Natural Language Generation of Compound-Complex Sentences for English-Hindi Machine Aided Translation, Vivek Mehta and Durgesh Rao, Symposium on Translation Support Systems, STRANS-03, 2003.

-7-

Centre for Development of Advanced Computing

Some significant past projects executed: 1. OCCPTS: Heuristic system for scheduling the movement of oil tankers for distributing  petroleum products across refineries and consumer ports. 2. PIPES: A heuristic search system for scheduling the pumping of various oil products through a  pipeline 3. Mathemagic: A remedial education system for 10th standard mathematics 4. Vidya: An immersive situated learning system for Hindi 5. Vidwan: A rule based expert system shell Any major achievements (restrict to 2-3): 1. The group organizes the well known biennial KBCS conference series on AI, and brings out the AI quarterly, Vivek. 2. Work in the group has resulted in four PhD theses and a number of Masters projects.

-8-

HP Labs India, Bangalore

Language Technology and Applications Information provided by : K.S.R. Anjaneyulu ([email protected]) Other senior members:Ramani Srinivasan ([email protected]) Website (for more information): http://www.hpl.hp.com/india Major focus areas of the group: Recognition and Ink Processing, Information Embedding on Paper. Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Generic Gesture and Character Recognition This project explores generic features and algorithms for recognition of isolated gestures and characters. A supporting activity is the creation of linguistic resources to support research in handwriting recognition in Indic scripts. Annotated handwritten datasets developed at HP Labs India as a part of this research activity are now publicly available. We also co-organized the IWFHR-10 On line Tamil Character Recognition Competition, an effort to establish the state of  the art in Tamil character recognition. Our research in this area focuses on the development of annotated datasets of handwriting in languages and scripts. We also look at recognition of specialized sets of gestures and shapes. The core research areas in this field are: analysis of writing styles to devise handwriting recognition strategies, training and objective evaluation of robust handwriting recognition algorithms, and other applications including script and writer identification. 2. Lipi Toolkit (http://lipitk.sourceforge.net) An offshoot of our work in handwriting recognition is the open source Lipi Toolkit, a collection of tools and algorithms for building handwriting recognition engines. The toolkit is being used internally as well, for example, for gesture recognition for the Gesture Keyboard, a text input solution for Indic languages. Our research in this area focuses on: graphical tools for handwriting data collection, scripts and graphical tools for the analysis of recognition accuracy and errors, algorithms for handwritten shape recognition, build scripts for building engines, and support for UNIPEN 1.0 and a standard shape recognition interface. The toolkit focuses on: supporting collaborative HWR  R&D in academic and industrial settings, tools for user interface research, supporting commercial HWR development, promotion of standard ink representations and interfaces,   promotion of sharing & reuse of tools, algorithms, code and handwriting datasets, and  promotion of product and solution development. 3. Document Image Processing  The work on Document Image processing focuses on document image repurposing, document retrieval and archival targeted at HP's imaging devices. In emerging markets many enterprises

-9-

HP Labs India, Bangalore are paper centric and extend opportunities to develop solutions for digital image analysis which is the core area of our research program. Our research in this area focuses on solutions based on analysis of digital images of paper documents. The interesting part of the research is that it retains the end-user's present method of working on paper. The core research areas in this field are: detection and segmentation of various objects in the document image, image registration and ink extraction, and document image matching. 4. Security of Paper Documents Security practices for paper documents are required in order to authenticate the document source, the integrity of the information, and to distinguish copies from originals. We are  presently exploring various approaches to achieve this. The security practices for paper are highly important in a variety of applications. An example for this is the delivery of Government services through privately operated kiosks. Our research in this area deals with increasing the density of information that can be embedded on paper, being able to deal with documents that have pictures and different scripts, ensuring tamper-proof security of these documents, etc. Most recent 3-5 publications: Please visit http://www.hpl.hp.com/india Some significant past projects executed: Please visit http://www.hpl.hp.com/india Any major achievements (restrict to 2-3): 1. Gesture Engine that drives the Gesture Keyboard an innovation from HP Labs India that won the Wall Street Journal's Runners-up Prize in 2006 2. Lipi Toolkit described above. A toolkit that fosters research in Handwriting recognition.

- 10 -

Indian Institute of Management Calcutta, Kolkata

Management Information Systems Information provided by : Amitava Bagchi ([email protected]) Other senior members: Ambuj Mahanti ([email protected]), Asim K. Pal ([email protected]), Anup K Sen ([email protected]), Subir Bhattacharya ([email protected]), Rahul Roy ([email protected]), Somprakash Bandyopadhyay ([email protected]), Debashis Saha ([email protected]), Uttam Sarkar ([email protected]), S. D. Vaidya ([email protected]), Parthasarathi Dasgupta ([email protected]) Major focus areas of the group : AI Search Methods, Game Playing, Combinatorial Optimization, Soft Computing and Artificial Neural Networks, Constraint Satisfaction Problems, Workflow, Insurance, (Internet) Auctions etc.

- 11 -

Indian Institute of Technology Madras, Chennai

Department of Biotechnology Information provided by : Mukesh Doble ([email protected]; [email protected]) Website (for more information): http://www.biotech.iitm.ac.in/old/faculty/md.php Major focus areas of the group : Neural Networks, Principal Component Analysis Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Development of Quantitative structure activity relationships between structural/molecular  descriptors and biological activity using neural network models. 2. Analysis of ECG and EEG signals using neural network models for disease prediction and classification. Most recent 3-5 publications : 1. Mukesh Doble (2005). Mathematical Modelling of Production of Hydantoinase, Asia-Pacific Biochemical Engineering Conference, Korea, 16 May, 2005. 2. Venkata Rama Saran Kishore, Ravi Kishore, Mukesh Doble (2004). Neural Network modelling of Hydantonase production, International Conference on Intelligent Sensing, Madras, Jan 2004. 3. S S Bhagwat, H S Bevinakatti, Mukesh Doble (2005). Transesterificatin of substituted ethanols with ethyl acetate-modelling studies, J Biochem Engg (2005), 22 (3), 253-260.

Computer Science and Engineering Information provided by : Hema Murthy Other senior members: B Ravindran, Deepak Khemani Major focus areas of the group : Automatic Speech Recognition, Automatic Speech Synthesis, Reinforcement machine learning, Case based reasoning, Planning and Constraint Satisfaction.

Interactive Intelligence Laboratory (Part of the Reconfigurable and Intelligent System Engineering (RISE) Group) Information provided by : B. Ravindran ([email protected]) Other senior members: Vimal Mathew ([email protected]), Shravan Mathur Narayanamurthy ([email protected]), Munu Sairamesh ([email protected]), B. H. Sreenivasa Sarma ([email protected]), Sriram Raghavan ([email protected]), M. Saravanan

- 12 -

Indian Institute of Technology Madras, Chennai ([email protected]), P. Swapna Raj ([email protected]), Dinakar ([email protected]), Aakanksha Gagrani ([email protected])

Jayarajan

Major focus areas of the group : Machine Learning, Knowledge Representation, Data Mining,  Natural Language Processing (Information Extraction, Document Summarization) Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Reinforcement Learning - Issues in Knowledge Representation and Scaling up. Reinforcement Learning (RL) is a trial and error learning paradigm, built on foundations from  behavioral psychology, operations research and optimal control theory. We are focusing on extending RL algorithms to more complex problem domains, by incorporating richer  representations of the world, complex control architectures, multi-agent systems, and developmental models of learning. 2. Data Mining, Information Extraction, and Document Summarization. We are engaged in several problems related to information extraction: including the use of  recent advances in graphical models for document summarization and image labeling; structured multi-document summarization; and relevance measures for data mining.

- 13 -

Indian Institute of Technology, Kanpur 

Department of Computer Science and Engineering Information Provided by : Amitabh Mukherjee ([email protected]) Other senior members: Harish Karnick ([email protected]), R.M.K. Sinha ([email protected]) Major focus areas of the group : Natural Language Processing, Vision, Soft Computing, Robotics, Reasoning. Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Spatial Reasoning, Spatial Reconstruction and Story Animation from Linguistic Models, Interval Algebra based reasoning. 2. Robotics: Qualitative models of re-grasping, Soccer Playing Robots, vehicle motion planning. 3. Automated & Commonsense Reasoning. 4. Application of AI techniques to document processing, text recognition, computer vision, speech  processing, natural language processing and design of knowledge based systems. Application of  artificial neural networks and fuzzy computing techniques in pattern recognition. In natural language processing, one of the primary aims is to design machine aids for translation from English to Indian languages & vice-versa and among Indian languages. R.M.K. Sinha's approach is based on a new concept of using Pseudo-Interlingua, word expert model utilizing Karak  theory, pattern directed rule base and hybrid example base. His investigations also include exploring design and development of special parallel architectures for computer vision and natural language processing. R.M.K. Sinha has been working on R & D for Indian Language Technology for the last three decades and his research has touched and provided direction to almost all facets of providing technological solution to the problem of overcoming the language  barrier in the country. The multi-lingual GIST technology and several other packages for Indian language processing have been developed under his supervision.

Department of Electrical Engineering Information provided by : P.K. Kalra ([email protected]) Other senior members: Laxmidhar Behera ([email protected]), S.C. Srivastava ([email protected]), S.N. Singh ([email protected]) Website (for more information): http://www.iitk.ac.in Major focus areas of the group : Expert Systems applications, Cognitive Modeling, Soft Computing

- 14 -

Indian Institute of Technology, Kanpur 

Brief description of at most three projects in progress:  Please include any website for more details for each project  1. KARMAA (Knowledge Acquisition, Retention, Management, Assimilation & Application) 2. Intelligent Control 3. Quantum Learning System

Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory (KanGAL) Information provided by : Kalyanmoy Deb ([email protected]) Website (for more information): http://www.iitk.ac.in/kangal/ Major focus areas of the group : Optimization problems in engineering design, Multi-objective Evolutionary Algorithms, Machine Learning Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Multi-objective evolutionary optimization (NSGA-II) 2. Robust and reliability based multi-objective design optimization 3. Constraint handling using evolutionary optimization Most recent 3-5 publications: 1. Deb, K., Mitra, K., Dewri, R. and Majumdar, S. (2004). Towards a better understanding of the epoxy polymerization process using multi-objective evolutionary computation. Chemical Engineering Science, vol. 59, number 20, 4261—4277. 2. Deb, K., Anand, A., and Joshi, D. (2002). A computationally efficient evolutionary algorithm for  real-parameter optimization. Evolutionary Computation Journal, vol. 10, number 4, 371--395. 3. Deb, K., Pratap. A, Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, vol. 6, number 2, 181--197. 4. Deb, K. and Srinivasan, A. (2006). Innovization: Innovating design principles through optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2006), New York: The Association of Computing Machinery (ACM), (pp. 1629--1636). 5. Deb, K. and Sundar, J. (2006). Reference point based multi-objective optimization using evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), New York: The Association of Computing Machinery (ACM), (pp. 635--642). Some significant past projects executed : 1. Evolutionary optimization for continuous variables

- 15 -

Indian Institute of Technology, Kanpur  2. 3. 4. 5. 6.

Multi-modal optimization Multi-objective test problem design Evolutionary multi-objective optimization and decision-making Various applications of optimization methodologies Evolutionary optimization with theoretical basis

Any major achievements (restrict to 2-3): 1. S. S. Bhatnagar award and Thomson Citation Laureate Award to Prof. K. Deb. 2. NSGA-II paper judged as the fast-breaking paper in engineering in Feb'04 by ISI Web of  Science.

- 16 -

Indian Institute of Technology, Kharagpur 

Computer Science & Engineering Information provided by : Anupam Basu ([email protected]), Sudeshna Sarkar  ([email protected]) Other senior members: Pabitra Mitra([email protected]) Website (for more information): http://www.facweb.iitkgp.ernet.in/~sudeshna/ Major focus areas of the group : Natural Language Processing, Search, Machine Learning, Planning Brief description of at most three projects in progress :  Please include any website for more details for each project  1. Natural language processing - various tools for Bengali. Working on two major projects: Indian language to Indian language machine translation and cross language information retrieval. 2. A recommendation system framework that makes recommendations for text documents and works with dynamically changing content and shifting user interests. Project: minekey (http://www.minekey.com/) 3. Multi-modal Participatory Content Repository for the Education of Rural Children, sponsored  by Media Lab Asia Most recent 3-5 publications: 1. Monojit Choudhury, Anupam Basu, Sudeshna Sarkar(2006). Multi-Agent Simulation of  Emergence of Schwa Deletion Pattern in Hindi, The Journal of Artificial Societies and Social Simulation (JASSS), Volume 9, Issue 2, March 2006. 2. Monojit Choudhury, Rahul Saraf, Vijit Jain, Sudeshna Sarkar and Anupam Basu (2007). Investigation and Modeling of the Structure of Texting Language JCAI-2007 Workshop on Analytics for Noisy Unstructured Text Data, Hyderabad, India, Jan 2007. 3. Devshri Roy, Sudeshna Sarkar and Sujoy Ghose (2005). Automatic annotation of documents with meta data for use with tutoring systems, 2nd Indian International conference on AI, IICAI 2005.

Department of Civil Engineering Soft Computing in Civil Engineering Information provided by : Sudhirkumar [email protected], [email protected])

- 17 -

Barai

([email protected],

Indian Institute of Technology, Kharagpur 

Website (for more information): http://softcomputing.tripod.com Major focus areas of the group : Soft Computing, Data Mining, Cellular Automata Brief description of at most three projects in progress :  Please include any website for more details for each project  1. Cellular Automata in Structural Optimization Structural analysis and design optimization is important to a wide variety of disciplines. The curent methods for optimization process require significant time and computing resources. By implementing cellular automata(CA) simulations, structural analysis and design optimization can be performed significantly faster than conventional methods. The main objective of the work was to study shape and topology optimization process for structures by using the concept of cellular automata. Considering the cells as the finite elements, the stress analysis is carried out  by the finite element method (FEM). The design variables were modified by applying a local rule to the stress states of the updated cell and its neighbouring cells. Design studies based on topology optimization and thickness sizing are performed; results demonstrate the applicability of the cellular automata environment for efficient optimal design of structures. 2. Parallel Neuro Simulator for Structural Engineering Problems Many researchers have demonstrated successful applications of neuro regression and/or  classification models for structural analysis, design, diagnostics and control problems. In the course of neuro data modeling many neural network models could be created at the cost of  computing time. Sometimes it may take days together for training a network for particular  structural engineering problem. When a particular neural model is selected based on the   performance, others are discarded. In this process, one can loose too much of time in computing and important information may go undetected. The research work was an attempt to demonstrate the problems of structural engineering by multiple neural network schemes on PARAM 10000. The study problems were: material behaviour modeling and weld defect classification. 3. Parallel Genetic Algorithm for Structural Optimization With the use of computer aided search tools like genetic algorithms, solutions to non-linear  structural optimization problems can be obtained easily and efficiently. The ever-growing need for more powerful computational resources has led to the parallelization of conventional genetic algorithm. The work involved development of two versions of genetic algorithms in parallel  processing environment. The first version had independent populations thriving at each node. The second version further incorporated a migration scheme in which fitter elements migrate  between the nodes after specified generations. Complete codes applicable to the parallel setup of PARAM 10000 have been developed, incorporating latest proposals for selection and constraint handling. A comparative study had been carried out for the singly reinforced beam cost optimization according to Indian Standards. An extension of the second version, which is independent from pre-specification of mutation and crossover rates, had also been studied. Most recent 3-5 publications: 1. S V Barai and Piyush Agrawal (2006). Parallel Neuro Classifier for Weld Defect Classification,

- 18 -

Indian Institute of Technology, Kharagpur  Abraham, A.; Baets, B.d.; Köppen, M.; Nickolay, B. (Eds.) Applied Soft Computing Technologies: The Challenge of Complexity, Springer 2006, ISBN: 3-540-31649-3 2. S V Barai (2006). Material Behaviour Modelling using Machine Learning Model, Divisional Journal – Civil Engineering, Institution of Engineers (IEI), Vol. 87, November 2006, pp:59-66. 3. R. Rajarao Dharmacherla and S V Barai (2005). Structural Optimization using Cellular  Automata, International Journal of Lateral Computing, Vol. 2, No. 1, December 2005, ISSN 0973-208X, pp-14-21. 4. A K Dikshit, S V Barai and Sameer Sharma (2005). A study on air quality prediction: An opportunistic Neuro-Ensemble Approach, Journal of Environmental Systems Vol. 30, No. 3,  pp:17-31. 5. P Sriram and S V Barai (2005). Weld Defect Classification using Wavelet Neural Networks, International Journal of Lateral Computing, Vol. 1, No.1, May 2005, pp:15-22, ISSN: 0973208X Some significant past projects executed: 1. Material Behavior Modeling  Material behavior modeling involves the development of mathematical models based on experimental data, experts' observations and reasoning. Against the rigorous iterative exercise of developing mathematical models, machine learning (ML) model - neural networks (NN) offer  a fundamentally different and appealing approach to the derivation and representation of  material behavior relationships. Such networks would contain sufficient information about the material behavior complexities, non-linear characteristics, stress strain behavior, material   properties etc. Further, these networks could be used effectively as material model to reproduce the trained experimental data and untrained experimental data. The work was towards identification of comprehensive data set and developing a systematic approach for  material model using NN. Demonstration examples of this study were taken up using the experimental data of shear behavior of reinforced concrete beam under effects of fire. 2. Liquefaction Potential Assessment  Liquefaction potential assessment has been a very important problem from the point of view of  geotechnical engineering. It is well known that many factors such as soil parameters and seismic characteristics influence this problem. Various researchers have attempted to solve this problem using artificial neural networks (ANN). However, many authors have missed important issues such as proper data modeling, ANN model selection, and performance evaluation of ANN for  liquefaction potential assessment. Covering these aspects, the study was carried out to model liquefaction potential data using a ML classifier. 3. Weld Defect Classification Maintenance of complex welded structures such as pressure vessels; load bearing structural members and power plants has long been recognized. The commonly used approach is nondestructive evaluation (NDE) of such welded structures. The work was towards an application of Probabilistic Neural Networks (PNN) Model for weld data extracted from reported radiographic images. The study highlighted better performance of PNN model in comparison to other well-reported models such as backpropagation neural networks (BPNN) and Linear  Vector Quantization (LVQ) Models. Further, PNN model was advocated as automated weld defect classifier.

- 19 -

Indian Institute of Technology, Kharagpur  4. Artificial Neural Networks (ANN) Based Ozone Forecast Using Moving Window Modelling  A new concept named, moving window modelling concept was used in combination with ANN for developing ozone concentration forecast models. The effects of different input combinations on ozone forecast were evaluated. The data of Maidstone from 16/04/2000 (midnight) to 31/12/2001 (11:00 P.M.) was used for the model development. The model was trained with 13480 training examples and tested at 1496 independent test points. The models’ performances were evaluated with wide variety of statistical parameters. The index of agreement (d2) was found to be in the range of 0.88 and 0.9883 with mean percentage absolute error (MPAE) varying between 14.7% and 46.56%. Historical concentrations of NOx, CO did not improve the models’ performance much in terms of index of agreement (d2) but had shown considerable effect on mean percentage absolute error (MPAE). The historical values of ozone concentration  proved to be most important input and historical values of NO2 concentration proved to be the second most important input parameter in ozone forecasting. Any major achievements (restrict to 2-3): Best Paper Award: 1st World Congress on Lateral Computing, December 2004

- 20 -

Indian Institute of Technology, Guwahati

Department of Computer Science and Engineering Information provided by : Shivashankar B. Nair ([email protected]) Website (for more information): http://www.iitg.ernet.in/engfac/sbnair/public_html/projects.htm Major focus areas of the group: Robotics, Soft Computing, Speech Processing, Agents, Natural Language Processing. Brief description of at most three projects in progress:  Please include any website for more details for each project  The major work being carried out is in the domain of Intelligent Mobile Robotics where symbol grounding is imperative. A working prototype, featuring simple speech to text interface cascaded with a natural language processor for conversion to robot-comprehensible commands was initially developed. Deploying robots on the network became a primary concern.

Robots in the Net (RobIN) and RobIN-II were some of the initial attempts at facilitating an easyto-use deployment procedure. Later a feature to facilitate intelligence sharing amongst robots over  the network (intelligent Robots in the Network: iRobIN), was incorporated. Further to allow untethered robots to roam around and communicate with each other a Mobile Ad hoc Network of  Intelligent Robots (MANER) was also built. Attempts are now being made to facilitate robots within MANER to exchange/share intelligence amongst themselves. If more information is required to cope with a problem at hand, they will attempt and reach out to others (robots) on the wired network using the iRobIN framework. Current work involves the building of a RoboSapienNetwork which finds its roots on iRobIN, MANER and SapienNet. The latter is a human network that allows for unobtrusive discovery of information of people in the immediate neighbourhood. It is envisaged that RoboSapienNet will allow both humans and robots to interact and share intelligence and together form a congenial society. Apart from this, we have used several bio-inspired techniques for intelligent control of robots. Work on the use of artificial immune systems to make robots assist, co-operate and co-exist, has also been carried out. Robot control techniques using Artificial Neural Networks and Genetic algorithms to learn to avoid obstacles (on ground and space), to learn gaits, etc., have also been tried and tested on real robots. It is envisaged that these techniques will be embedded on robots that populate the RoboSapienNet implementation and eventually form a society of intelligent, cooperative robots capable of co-existing with the human beings. Besides these, other activities comprise the use of Genetic Algorithms, Artificial Neural Networks and Fuzzy logic in a variety of intelligent applications including roller bearing dimension optimization, question-answering systems, searches on the web, sensorimotor control and text  processing for both English and Indian languages. - 21 -

Indian Institute of Technology, Guwahati

 Relevant URLs: iRobin and MANER: http://www.iitg.ernet.in/engfac/sbnair/public_html/iRobIN/robin-index.htm RobIN: http://www.iitg.ernet.in/engfac/sbnair/public_html/RobIN/Default.htm RobIN-II: http://www.iitg.ernet.in/engfac/sbnair/public_html/iRobIN/rajusbnair-badapanda.pdf  MANER and SapienNet: http://www.iitg.ac.in/engfac/sbnair/public_html/SapienNet.pdf  Most recent 3-5 publications: 1. Shivashankar B. Nair, K V D Pradeep Kumar, M. Saravanan, "A Communication Protocol for a Mobile Adhoc Network of Robots", Proceedings of the The International Conference on Emerging Applications of IT (EAIT 2006), Science City, Kolkata, India, 10-11, February 2006,  pp. 223-226, Published by Elsevier. 2. Nandan Chaturbhuj, Shivashankar B. Nair , "A Co-operative Intelligent Assisting Agent Architecture for Web Searching and Desktop Management", The Eighth Pacific Rim Workshop on Multi-Agents, PRIMA 2005, 26th - 28th September 2005, Kuala Lumpur, Malaysia (To appear in the Lecture Notes on Artificial Intelligence, Springer Verlag) 3. Chingtham Tejbanta Singh, Shivashankar B. Nair "An Artificial Immune System for a Multi Agent Robotic System", Transactions of the International Academy of Sciences: Enformatika, Vol. 6, June 2005, pp. 308-311. 4. Nair, S.B., Toppo N., "A Framework for Sharing Intelligence Among Mobile Robots on a   Network", Session on Network based Intelligent Control, Proceedings of the 2nd IASTED International Multi-Conference on Automation, Control and Information Technology (ACIT 2005) Novosibirsk, Russia, 20- 24th June 2005, pp.93-98. Some significant past projects executed:  Intelligent Assisting Agent for Microsoft Windows Desktop (http://www.iitg.ernet.in/engfac/sbnair/public_html/agent_web_page/index.htm) This project sponsored by Microsoft Corp. USA under their Academic Alliance programme was aimed at making a plug-in Assisting Agent for the Windows desktop environment that can, in due course, take over and learn the user's behaviour. The agent learns to mimic a user's desktop  behaviour, schedule tasks, check and filter spam and even comprehend interests and explore further  on the web for relevant content. Once the profile is built and learnt, the agent behaves semi-autonomously, emulating the user and seeking his/her inputs for possible deviations. The profile is something that is continuously updated and learnt. With multiple users, multiple assisting agents come into the scenario and exchange the learned user profiles, thereby removing redundancy by co-operation. The system uses the  blackboard architecture with multiple agents co-operating to solve these tasks.

Electronics and Communication Engineering (ECE) Information provided by : S. Dandapat ([email protected]) Other senior members: J. S. Sahambi([email protected]), H. Nemade ([email protected]), S. R. M. Prasanna ([email protected]), K. S. Rao ([email protected])

- 22 -

Indian Institute of Technology, Guwahati

Major focus areas of the group: Speech Processing, Artificial Neural Networks, Support Vector  Machines. Brief description of at most three projects in progress :  Please include any website for more details for each project  The objective of this group is to develop new methods for processing signals like biomedical and speech signals. This involves exploring different signal processing tools, pattern recognition tools and artificial intelligence (AI) techniques for developing better methods of extracting features and modeling them from these signals. The developed features and models may be used in different tasks, which aid the researchers in these fields, in particular, and society, in large, for better  understanding, interpretation and developing signal processing systems for the usage in different  practical applications. In this direction some of the current research works in this group include, extracting prosodic information like pitch and duration from the speech signals using neural network and support vector machines, extracting speaker-specific information from the excitation source component of speech signal using neural network models, speaker recognition studies using neural network models and exploring new analysis methods, features, modeling techniques and comparison techniques using different AI techniques. We are also developing new methods for  extracting features from biomedical signals using AI techniques.

- 23 -

Indian Institute of Technology-Bombay, Mumbai

Department of Computer Science and Engineering Natural Language Computing

Processing

with focus

on

Indian

Language

Information provided by : Pushpak Bhattacharyya ([email protected]) Other senior members: Vaijayanthi Sarma ([email protected]), ([email protected]), Krithi Ramamritham([email protected]), ([email protected]), Rajat Mohanty ([email protected])

Milind Om

Malshe Damani

Website (for more information) : http://www.cse.iitb.ac.in/~pb (for publications) http://www.cfilt.iitb.ac.in (for resources and description of projects) Major focus areas of the group : Semantics processing; Machine Translation among English, Hindi and Marathi; Wordnets; Word Sense Disambiguation; Cross Lingual Information Retrieval; MT Evaluation Brief description of at most three projects in progress :  Please include any website for more details for each project  1. Wordnets for Hindi and Marathi These highly important lexical resources are being created to capture lexical semantics leading to Indian language processing. At the time of reporting, the Hindi wordnet has approximately 22000 synsets (about 35000 unique words) and the Marathi wordnet has approximately 8000 synsets (about 16000 words). The former is being used for automatic lexicon generation and word sense disambiguation tasks. Please visit the websites mentioned above for more information. 2.  Interlingua based machine translation for English, Hindi and Marathi We use the Universal Networking Language (UNL) framework (http://www.undl.org) which is an interlingua. UNL is an effective vehicle for representation of semantics. The current focus is English analysis and Hindi-Marathi generation. The novel idea of semantically relatable sequences is being pursued in this context. Divergence phenomena among the three languages mentioned is also a focus of study. 3.  AgroExplorer/aAQUA- Multiligual, Meaning Based Search in a question answer forum This is in the area of cross lingual, high accuracy retrieval in the agricultural domain. Farmers' questions and experts' answers are stored in semantic graph form and search is carried out in this knowledge base, corresponding to user queries. Please visit http://www.mlasia.iitb.ac.in. Most recent 3-5 publications: 1. Medimi Srinivas and Pushpak Bhattacharyya, A Flexible Unsupervised PP-Attachment Method

- 24 -

Indian Institute of Technology-Bombay, Mumbai

2. 3.

4.

5.

6.

Using Semantic Information , IJCAI 2007, Hyderabad, India, Jan, 2007. Debasri Chakrabarty, Vaijayanthi Sarma and Pushpak Bhattacharyya, Complex Predicates in Indian Language Wordnets , Lexical Resources and Evaluation Journal, Accepted. Sanjeet Khaitan, Kamaljeet Verma and Pushpak Bhattacharyya, Exploiting Semantic Proximity for Information Retrieval, IJCAI 2007, Workshop on Cross Lingual Information Access, Hyderabad, India, Jan, 2007 R. Ananthakrishnan, Pushpak Bhattacharyya, M. Sasikumar and Ritesh M. Shah, Some Issues in Automatic Evaluation of English-Hindi MT: More Blues for BLEU, ICON 2007, Hyderabad, India, Jan, 2007 Krithi Ramamritham, Anil Bahuman, Subhasri Duttagupta and the aAQUA Team 2006, aAqua: A Database-backended Multilingual, Multimedia Community Forum, ACM SIGMOD International Conference on Management of Data, Chicago Kuhoo Gupta, Manish Shrivastava, Smriti Singh and Pushpak Bhattacharyya, Morphological Richness Offsets Resource Poverty- an Experience in Building a POS Tagger for Hindi , COLING/ACL-2006, Sydney, Australia, July, 2006.

Some significant past projects executed: 1. Technology Development in Indian Languages with focus on Marathi and Hindi 2. UNL project funded by the United Nations Any major achievements (restrict to 2-3): A very successful symposium called "Modeling and Shallow Parsing of Indian Languages" was conducted in March, 2006, which brought together NLP and linguistics researchers from all over  the country. Please visit http://www.cfilt.iitb.ac.in/~mspil-06/.

School of Biosciences and Bioengineering Intelligent Systems Information provided by : S. Arunkumar ([email protected]) Website (for more information) : http://www.btc.iitb.ac.in/~sak  Major focus areas of the group : Learning; Intelligent Image Processing and understanding with relevance to GIS; Intelligent Systems in design, engineering, health-care, manufacturing, management, medicine, and resource analysis; Natural Language Processing; and Cognition. Brief description of at most three projects in progress :  Please include any website for more details for each project  1.  Learning Semantics: The process of reading text involves comprehension of read text through a series of steps that occur in the reader's brain. One of these steps involves the identification of the forthcoming word in the read text. This depends on the size and content of the short-term memory and cooccurrence of the word being predicted with other words. The decrease in the size of the short-

- 25 -

Indian Institute of Technology-Bombay, Mumbai term memory possibly due to brain damage leads to reading disorder called Dyslexia. This semantics model using the short-term memory and co-occurrence may be implemented using a neural network. The network weights are initialized in a non-random manner based on problem domain, which leads to significant lower minimum error. The work is applicable to text classification yielding better classification accuracy than using support vector machines. 2. Heuristics in Scheduling : Resource-constrained scheduling and resource-minimization problems are NP-complete   problems and the use of domain knowledge with sound heuristics could effect significant improvements. We consider the class of window-constrained capacitated and non-capacitated routing problems as those that may arise in vehicle routing and data networks with possibly time-varying demands and delays. The local routes are generated using criticality measures whereas the long-haul routes are generated using existing routes merger and templates generation. A novel algorithm for determining dynamic shortest paths is obtained that is   particularly suitable for data communication and road networks with varying traffic, and dynamic updates. 3. Optimal Drug Dosage with Relevance to Hypertension: Drug design and dosage are two sides of the multi-sided complex problem in patient management. This will have to take into account genetic, physiological and mental aspects of   patients as well as the specific pathways by which the drug operates. Optimal drug dosage for  hypertension was considered in a two-compartment model framework. Most recent 3-5 publications: 1. P. C. Prasad and S. Arunkumar, "From Short-Term Memory to Semantics - A Computational Model," Neural Computing and Applications,13(2), pp.157-167, 2004 2. P. C. Prasad and S. Arunkumar, "Co-occurrence Based Semantics in Lexical Access: A Model of Learning," Proc. First Indian International Conference in Artificial Intelligence (IICAI-03), Dec. 2003 3. S. S. Dighe, S. Arunkumar and M. A. Gadkari, "Case-Based Learning for Prediction of PostMyocardial Infaction Outcomes," Proc. IEEE BMES-EMBS99 Conference, Atlanta, Georgia, USA, October 1999 4. S. Arunkumar, P. R. Warkhede, and S. A. Seshia, An Architecture for Intelligent Information Retrieval, Bell Laboratories, June 1998 5. S. Arunkumar and T. Chockalingam, "Genetic Search Algorithms and their  Randomized Operators," Computers Math.Applic.,Vol.5, No.5, pp.91-100, 1993. Some significant past projects executed: 1. Optimal Routing of Postal Vehicles, Dept. of Posts, Govt. of India 2. Intelligent Systems in Logistics, BSES Ltd., Mumbai 3. Ruwats: A Decision Support System for Rural Water Supply, Govt. Of Maharashtra 4. Strategic Mobile Communication, Govt. of India Any major achievements (restrict to 2-3): 1. Commendation from the Government from the Govt. of India for contribution to strategic communication.

- 26 -

Indian Institute of Technology-Bombay, Mumbai 2. Deployment of the Ruwats system in Maharashtra.

Centre for Data Engineering Information provided by : Kamalakar Karlapalem ([email protected]) Other senior members: Vidit Bansal ([email protected]), Vikram Pudi ([email protected]), P Krishna Reddy ([email protected]), Soujanya Vadapalli ([email protected]), Lini Thomas ([email protected]), Satyanarayana Valluri ([email protected]) Major focus areas of the group : Agents, Data Mining Brief description of at most three projects in progress :  Please include any website for more details for each project  1. Multi Agent Systems – dealing with RoboCup RoboRescue and RoboSoccer competitions. Kshitij – IIIT RoboRescue Team - secured 3rd position in RoboCup 2005 RoboRescue Simulation Competition. The group has worked on traffic simulation using multi-agent systems, simulating complex systems such as computer networks for behavior of virus and worm attacks and developing technologies for massively multi agent systems. 2. Data Mining activities include work on data clustering algorithms and graph mining algorithms. Developed approaches for time-series mining, and mining mathematical equations. We have also developed a toolkit to generate synthetic data sets for testing clustering algorithms. Algorithms for finding association rules, web community extraction, recommendation systems, and search algorithms are other sub areas of research.

Centre for Visual Information Technology (CVIT) Information provided by : PJ Narayanan ([email protected]) Other senior members: CV Jawahar ( [email protected]), ( [email protected]), Anoop Nambodari ([email protected])

Jayanti

Sivaswamy

Major focus areas of the group : Character Recognition, Document Image Processing, Vision Brief description of at most three projects in progress :  Please include any website for more details for each project  CVIT was established with the mission to do fundamental and applied research in the area of Visual Information Processing, which includes Image Processing, Computer Vision, Multimedia, Computer Graphics, and Virtual Reality. The centre presently focuses on (a) Document Image Understanding (b) Content Based Retrieval of Multimedia Data (c) VR & Visualization (d) Video Processing and (e) Geometric Solutions to Computer Vision Problems.

- 27 -

International Institute of Information Technology, Hyderabad CVIT is actively involved with building tools for document understating tasks with special emphasis on the Indian context. These include extensible, multi-lingual OCR systems, form  processing systems, document database systems that can index scanned documents, reading aid for   printed text, and on-line character recognition systems. Advanced prototypes of many of these systems have been demonstrated at the institute's open house and other forums. The centre believes that the rich, multi-lingual scenario existing in India provides special challenges to document understanding research.

Language Technologies Research Centre Information provided by : Rajeev Sangal ([email protected]) Other senior members: Dipti Misra Sharma ([email protected]), Vasudeva Varma ([email protected]), S P Kishore ([email protected]), Lakshmi Bai ([email protected]), Bipin Indurkya ([email protected]), Soma Paul ([email protected]), B Yegnanarayana ([email protected]), Suryakanth V. Gangashetty ([email protected]), Sriram V ([email protected]), Anil Kumar Singh ([email protected]) , Samar Husain ([email protected]), Rafiya Begum ([email protected]), M Prashanth Reddy ([email protected]), Jagdeesh Yadav ([email protected]) and Arafat Ahsan ([email protected]) Website (for more information) : http://ltrc.iiit.ac.in Major focus areas of the group : Natural Language Processing, Search Engine, Information Retrieval and Speech Processing

The aim here is to develop aids for overcoming language barriers and make on line text in any subject domain accessible to readers across languages. A major project is development of Shakti machine translation system, which translates text in English to various Indian and other world languages. Work is in progress on parsing of English and Indian languages including shallow  parsers. Effort is on developing taggers, chunkers, statistical parsers, pp-attachment, semantic role labeling etc. Machine learning techniques are being extensively used. Other related activity is for    building bilingual and monolingual lexical resources, annotated corpora including tree-banks, grammars, transfer grammars, etc. Brief description of at most three projects in progress :  Please include any website for more details for each project  The following three projects are in progress in Natural Language Processing area. 1. IL-ILMT: Indian Language to Indian Language Machine Translation System Funded by: Ministry of Communications & Information Technology, Govt of India The objective of this project is to develop machine translation system from Indian languages to Indian languages. The systems would be real-life systems, with a given level of translation accuracy, and they would be capable of further improvement using machine learning techniques. The domains of ILMT system are Tourism and Health. It will also lead to the development of basic tools and lexical resources for Indian languages, such as POS taggers,

- 28 -

International Institute of Information Technology, Hyderabad chunkers, morph analysers, bidirectional bilingual dictionaries, annotated corpora, etc. The system would be so designed that it would be scalable, and that it would be robust even if  given sentences are outside the domain. Although, a general purpose system would be developed, it will be trained on the above two chosen domains. Its performance would be higher with respect to the chosen domains. 2. E-ILMT: Development of English to Indian Language Machine Translation System Funded by: Ministry of Communications & Information Technology, Govt of India The IIIT-Hyderabad is part of the MCIT, Govt. of India sponsored consortia project on  building a deployable English to Indian Language Machine Translation system. The project aims at designing, developing and deploying Machine Translation Systems from English to Indian Languages covering selected domains of correspondence/documents relating to Tourism and Agriculture Domain. By the end of 18-24 months the MT system is expected to give approximately 80-85% of accuracy. The Indian languages chosen for this project are Hindi, Bengali, Marathi, Oriya, Urdu and Tamil. 3. Development of Hindi syntactic tree banking: The aim of this project is to build a resource of tree banking by annotating Hindi Sentences with karaka relations. Most recent 3-5 publications : 1. Relative Compositionality of Noun - Verb Multi-Word Expressions in Hindi, Sriram V, Preeti Agarwala and Aravind K. Joshi, Published in the Proceedings of ICON-2005: International Conference on Natural Language Processing, 18 - 20 December, 2005, India. 2. HMM Based Chunker for Hindi, Akshay Singh, S M Bendre and Rajeev Sangal. Published in the Proceedings of IJCNLP-05: The Second International Joint Conference on Natural Language Processing, 11-13 October, 2005, Jeju Island, Republic of Korea . 3. Handling Multi-word Expressions without Explicit Linguistic Rules in an MT System, Akshar  Bharati, Rajeev Sangal, Dipti M Sharma, Sriram V and T Papi Reddy. In Proceedings of  Seventh International Conference on TEXT, SPEECH and DIALOGUE - 2004, Brno, Czeck  Republic. . 4. Generic Morphological Analysis Shell, Akshar Bharati, Rajeev Sangal, Dipti M Sharma and Radhika Mamidi. In Proceedings of LREC 2004 - SALTMIL Workshop: First Steps in Language Documentation for Minority Languages. Lisbon, Portugal. 24th-30th May 2004. . 5. Unit Selection Voice for Amharic Using Festvox, Sebsibe H/Mariam, Kishore S.P., Rohit Kumar, Alan W Black and Rajeev Sangal. Published in the Proceedings of 5th ISCA Speech Synthesis Workshop,14-16 June 2004, Pittsburgh, PA . 6. Experiments with Unit Selection Speech Databases for Indian Languages, S.P. Kishore, Alan W Black, Rohit Kumar and Rajeev Sangal. Presented in the seminar on Language Technology Tools: Implementation of Telugu, 2003, Hyderabad, India Some significant past projects executed: 1. Language Database Development for Example Based Machine Translation Funded by Department of Information Technology, Ministry of Communications & Information

- 29 -

International Institute of Information Technology, Hyderabad Technology 2. Development of Specialized Search Tools for Indian Languages based on NLP Funded by Dept of Science & Technology, Govt of India 3. RAVI - Reading Aid Software for visually impaired Funded by Ministry of Social Justice and Empowerment Any major achievements (restrict to 2-3): 1. Our system achieved the best ROUGE scores in DUC 2006 Summarization competition. (http://duc.nist.gov for more details) 2. LTRC/IIIT students achieved the best performing system for POS tagging and Chunking in the contest held as part of the IJCAI-07 Workshop on Shallow Parsing for South Asian Languages

Search and Information Extraction Lab Information provided by: Vasudeva Varma ([email protected]) Other senior members: Prasad Pingali ([email protected]), ([email protected]), Bhupal Reddy ([email protected])

Kula

Kukeba

Website (for more information): http://search.iiit.ac.in Major focus areas of the group: Information Retrieval, Information Extraction, Search, Natural Language Processing, Text Summarization, Document Categorization

Search and Information Extraction Lab (SIEL) focuses on solving research problems in the areas of  Information Retrieval and Extraction using NLP techniques. As a part of the Language Technologies Research Center (LTRC) at IIIT, this research group tries to address IR, IE and related problems leveraging on the Language Technologies research that happens in the other  groups at the LTRC. Major activities of this group include building search engines for Indian languages, domain specific search engines, personalized search engines, named entity extraction, automatic text summarization, focused web crawling and document categorization. Brief description of at most three projects in progress:  Please include any website for more details for each project 

CLIA – Development of Cross-Lingual Access System Funded by: Ministry of Communications & Information Technology, Govt of India Cross-language information Access (CLIA) is a sub field of information retrieval dealing with retrieving Information written in a language different from the language of the user's query. For  example, a user may pose their query in English but retrieve relevant documents written in Hindi. For example 1. User will be able to give a query in one Indian language and 2. User will be able to access documents available in a. The language of the query, - 30 -

International Institute of Information Technology, Hyderabad b. Hindi (if the query language is not Hindi), and c. English 3. All the documents would be presented to the user in the language of the query. The results can also be presented in the language in which the information originally resided. The languages involved will be Bengali, Hindi, Marathi, Punjabi, Tamil and Telugu. Most recent 3-5 publications: 1. "Query Independent Sentence Scoring approach to DUC 2006" Jagarlamudi Jagadeesh, Prasad Pingali, Vasudeva Varma. Document Understanding Conference, Query Independent Sentence Scoring approach to DUC 2006, New York, June 8th and 9th, 2006, held along with HLT/NAACL 2006. 2. "A Relevance-Based Language Modeling Approach to DUC 2005", Jagarlamudi Jagadeesh, Prasad Pingali, Vasudeva Varma. Document Understanding Conference, 9th October 2005 at Annual meeting of HLT/EMNLP, Vancouver, BC, Canada 3. "WebKhoj: Indian language IR from multiple character encodings" Prasad Pingali, Jagadeesh J, Vasudeva Varma. WWW-2006 23-26 May 2006 Edinburgh, Scotland, UK  4. “Building Large-scale ontology networks”, Vasudeva Varma, Language Engineering Conference, December 13-15, 2002, University of Hyderabad, IEEE Computer Society Publications, Pages: 121-127, ISBN 0-7695-1885-0 Some significant past projects executed: 1. Personalized search engines on mobile devices funded by Nokia Research Center, Finland 2. Information Extraction Engine for Disaster Management funded by ADRIN - Department of  Space, Government of India 3. Indian Language Search Engine funded by Department of Science and Technology, Govt. of  India.

Any major achievements (restrict to 2-3): 1. SIEL participated in Document Understanding Conference summarization tasks in years 2005 and 2006. In the year 2006, its summarizer is ranked number one in all categories of automated evaluations 2. SIEL is the only team to participate in CLEF-2006 (Cross Language Evaluation Forum) from India with entries in Telugu, Hindi and Oromo language ad hoc information retrieval tasks.

Speech Processing Group Information provided by: Kishore Prahallad ([email protected]) Other senior members: B. Yegnanarayana ([email protected]), Rajeev Sangal ([email protected]), Suryakanth V Gangashetty ([email protected]), Gopalakrishna A ([email protected]), Venkatesh Keri ([email protected]), Sachin Joshi ([email protected]), Santhosh Yuvaraj ([email protected]) , Raghavendra E ([email protected]), Srinivas Desai ([email protected]), Sebsibe H Mariam ([email protected])

- 31 -

International Institute of Information Technology, Hyderabad

Website (for more information): http://speech.iiit.ac.in Major focus areas of the group: Speech Recognition, Speech Synthesis, Dialog systems for  Indian Languages, Speaker Recognition, Speech Enhancement and Speech Signal Processing Brief description of at most three projects in progress:  Please include any website for more details for each project  The goal of the speech processing group is to build speech based interfaces for human-computer  interaction, providing access to digital content for illiterate and vision impaired people specifically in the context of India. The speech processing group is actively involved in various research and development projects such as Text To Speech (TTS) and Automatic Speech Recognition systems (ASR) for Indian languages.

 Naturally speaking TTS systems are built in Telugu, Hindi and Tamil, while large vocabulary ASR  systems are built in Telugu, Tamil and Marathi. Limited domain speech-speech translation systems from Tamil-to-Telugu, Telugu-to-Hindi have also been demonstrated. Limited domain dialog systems where user can interact with the machine in speech-in and speech-out mode are being attempted. Our research focus is on real-time, low-memory TTS and ASR engines specifically for Indian languages, dialog systems, extraction of linguistic knowledge for minority languages, acoustic modeling for minority languages, statistical based parametric and articulatory speech synthesis, sub phonetic modeling for capturing pronunciation variations. Most recent 3-5 publications: 1. Sebsibe H Mariam and Kishore Prahallad "Extraction of Linguistic Information with the aid of  acoustic data to build speech systems", in Proceedings of IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Hawaii, USA, 2007. 2. Kishore Prahallad, Suryakanth V Gangashetty, B.Yegnanarayana and Raj Reddy, "Problems and  prospects in collection of spoken language data", in Proceedings of International Conference on Universal Digital Libraries (ICUDL), Egypt 2006. 3. Kishore Prahallad, Alan W Black and Ravishankar Mosur, "Sub-phonetic modeling for  capturing pronunciation variations for conversational speech synthesis", in Proceedings of IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), France 2006. 4. Anumanchipalli Gopalakrishna, Rahul Chitturi, Sachin Joshi, Rohit Kumar, Satinder Singh, R.N.V Sitaram and S.P. Kishore, "Development of Indian Language Speech Databases for  Large Vocabulary Speech Recognition Systems", Proceedings of International Conference on Speech and Computer (SPECOM), Patras, Greece, Oct 2005. 5. S. P. Kishore, Alan W Black, Rohit Kumar and Rajeev Sangal, "Experiments with Unit Selection Speech Databases for Indian Languages", in Proceedings of National Seminar on Language Technology Tools: Implementations of Telugu, Hyderabad, India, 2003. Some significant past projects executed:

- 32 -

International Institute of Information Technology, Hyderabad 1. 2. 3. 4.

Text to speech for Telugu, Hindi and Indian English Speech Recognition Systems for Tamil, Telugu and Marathi Reading Aid for Visually Impaired Speech Synthesizers for hand-held devices

Robotics Research Center  Information provided by: K Madhava Krishna ([email protected] ) Other senior members: Bipin Indurkhya ([email protected]), Pandey([email protected]), Subhash ( [email protected])

Amit

K 

Website (for more information): http://www.iiit.net/research/irl/, http://research.iiit.ac.in/~viswanath/robotics/ Major focus areas of the group: Mobile Robotics, Multi Robotic Systems Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Micro robotic system development : Micro robot is a small versatile low cost robotic system. Its mechanical structure comprises holonomic drive system making it capable of navigating in tight space. Micro robot incorporates an array of various sensors for sensing its environment with high accuracy. It has scalable architecture in all the aspects viz. mechanics, electronics, and software. It can also be scaled in terms of number of robots in a swarm of robots. It is controlled from the program running on the computer via wireless communication. Basic functionalities like motion control, odometric position estimation, integration of sensor readings are implemented in the onboard controller board. Thus computer is freed up from micro managing the robot tasks and the amount of data between the computer and robot is reduced to large extent. 2. Sonar Mapping : (http://research.iiit.ac.in/~viswanath/robotics/sonar_map.html) We present a methodology for integrating features within the occupancy grid (OG) framework. The OG maps provide a dense representation of the environment. In particular they give information for every range measurement projected onto a grid. However independence assumptions between cells during updates as well as not considering sonar models lead to inconsistent maps. Feature based maps provide more consistent representation by implicitly considering correlation between cells as well as forward models of sensors. But they are sparse due to sparseness of features in a typical environment. We provide a method for integrating feature based representations within the standard Bayesian framework of OG and provides a dense and more accurate representation than standard OG methods. 3. Multi-sensor Surveillance: (http://research.iiit.ac.in/~viswanath/robotics/mul_survei.html) The objective here is to see what kind of coordination mechanisms between sensors enables optimal detection of targets moving across a surveillance area. Most recent 3-5 publications:

- 33 -

International Institute of Information Technology, Hyderabad 1. Ganesh P Kumar and K Madhava Krishna, "Optimal Multi Target Detection by Multiple Sensors   by Moving to the Maximal Clique in a Covering Graph", IJCAI 2007, accepted (oral  presentation) 2. K Madhava Krishna and Henry Hexmoor, "A framework for guaranteeing detection   performance of a sensor network", Integrated Computer-Aided Engineering Journal, Volume 12, Number 3 / 2005, Pages: 305-317, IOS Press 3. A K Pandey, K Madhava Krishna and Mainak Nath, "Integrating features onto an occupancy grid for sonar based safe mapping", IJCAI (International Joint Conference on AI) 2007 accepted 4. K. Madhava Krishna, R. Alami and T. Simeon, "Safe Proactive Plans and their Execution", Robotics and Autonomous Systems, 54 (2006) 244-255 (available online at www.sciencedirect.com) November 2005 Some significant past projects executed: 1. Text to speech for Telugu, Hindi and Indian English 2. Speech Recognition Systems for Tamil, Telugu and Marathi 3. Reading Aid for Visually Impaired 4. Speech Synthesizers for hand-held devices

- 34 -

Indian Statistical Institute, Kolkata

Computer Vision and Pattern Recognition (CVPR) Unit Indian Language, Script and Document Processing group Information provided by: Bidyut B Chaudhuri ([email protected]) Other senior members: S. K. Parui ([email protected]), U. Pal ([email protected]), U. Garain ([email protected]), U. Bhattacharya ([email protected]), M. Mitra([email protected]), S. Palit ([email protected]) Major focus areas of the group : Character Recognition, Natural Language Processing, Speech Processing, Cross Lingual Information Retrieval Brief description of at most three projects in progress :  Please include any website for more details for each project  The CVPR Unit has been engaged in processing and recognition of bio-medical, remotely sensed and forensic images, among others. This group is strong in developing pattern recognition techniques for various theoretical and practical applications. A special interest of this group is the development of document analysis systems for Indian scripts. The group has pioneered the development of Bangla and Devanagari OCR systems. Automatic Document text segmentation, handwritten character recognition, writer identification, postal address reading system and automatic table-form processing are current areas of interest of this group. Some workable Indian language Braille softwares (Bharati braille) were also developed and given to an organization for  distribution to the Blind schools of this country.

  NLP and speech analysis of Indian languages are among other areas of strong interest of this group. Development of Indian language Electronic dictionary, Spell-checker, morphological   processor etc were pioneered here. Language statistics, computational stylistics, multi-word expressions and anaphora resolution are some other topics on which this group is working recently. The first Bangla speech synthesizer was developed from this institute and our current interest is to analyse the prosody and intonation pattern of general Bangla spoken words. This group is also engaged in finding smart approaches for cross-lingual information retrieval, especially involving Bangla and Hindi documents.

- 35 -

Jadavpur University, Kolkata

Computer Science & Engineering Department Information provided [email protected])

by :

Sivaji

Major focus areas of the group: Answering Systems

Bandyopadhyay

([email protected],

Language Technology, Machine Translation, Question

Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Development of English to Indian Languages Machine Translation Systems (EILMT) 2. Development of Indian Language to Indian Language Machine Translation Systems (ILMT) 3. Development of Cross Lingual Information Access Systems (CLIA)

All these are consortium mode projects initiated by DIT, Government of India involving a number  of institutions at the national level. Jadavpur University is one of the partners of these three  projects. Prof. Sivaji Bandyopadhyay is the Chief Investigator of all these projects on behalf of  Jadavpur University. Most recent 3-5 publications: 1. A modified Joint Source-Channel model for Machine Transliteration, Asif Ekbal, Sudip Kumar   Naskar, Proc. of COLING-ACL 2006, Sydney, Australia, 2006. 2. Dialogue Based Question Answering System in Telugu, Rami Reddy Nandi Reddy and Sivaji Bandyopadhyay, Proc. of EACL06 Workshop on Multilingual Question Answering (MLQA06), 2006. 3. Handling of Prepositions in English to Bengali Machine Translation, Sudip Naskar and Sivaji Bandyopadhyay, Proc. of EACL 06 Workshop Third ACL-SIGSEM 06 Workshop on Prepositions, 2006. 4. A Phrasal EBMT system for translating English to Bengali, Sudip Naskar and Sivaji Bandyopadhyay, Proc. of MT SUMMIT X, 2005. 5. A semantics-based English-Bengali EBMT System for translating news headlines, Diganta saha and Sivaji Bandyopadhyay, Proc. of the 2nd EBMT Workshop in MT SUMMIT X, 2005.

- 36 -

National Chemical Laboratory, Pune

Chemical Engineering and Process Development Division Artificial Intelligence Systems Group (AISG) Information provided by : Sanjeev S. Tambe ([email protected]) Website (for more information): http://www.ncl-india.org/ Major focus areas of the group: Primary research activities of this group focus on both the theory and applications of various Artificial Intelligence (AI) paradigms viz. Artificial Neural Networks (ANNs), Genetic/Memetic Algorithms (GA/MA), Genetic programming, and Fuzzy Logic. These formalisms have been extensively used for developing new modeling, classification, data mining and optimization strategies for chemical, chemical engineering/technology, polymer and   biochemical processes. Specifically, AI-based applications have been designed, developed and validated for steady-state and dynamic process modeling, process identification, nonlinear process control, process monitoring, fault detection and diagnosis, process optimization, soft-sensor  development and input selection.

Other research interests of the AISG are: Control of nonlinear reacting systems, modeling using multi-variate statistical methods such as principal component analysis (PCA) and partial least squares (PLS), Modeling and classification using machine learning formalisms such as Support Vector Machines (SVMs) and Support Vector Regression (SVR). Brief description of at most three projects in progress:  Please include any website for more details for each project  The AISG has completed several contractual assignments involving AI-based modeling, classification, soft-sensor development and optimization of a variety of processes/products such as polyethylene manufacture, gas cracker, adhesives, carbonating towers, lime-kilns and fast moving consumer goods. The sponsors of these projects have been multi-national and mega Indian corporations as also public sector units and Government of India agencies.

A few of the AI-based applications developed by the AISG are listed below: 1. Robust nonlinear control with neural networks. 2. Counter-propagation neural network (CPNN) as a look-up table for process fault detection/diagnosis. 3. Artificial neural network assisted stochastic process optimization strategies. 4. Reaction modeling and optimization using ANNs and genetic algorithms: Case study involving TS-1 catalyzed hydroxylation of benzene. 5. Development of artificial neural network based process identification and model predictive control strategies for a pilot plant scale reactor. 6. Linear / nonlinear dimensionality reduction and feature extraction using conventional and AI- 37 -

National Chemical Laboratory, Pune   based formalisms such as PCA, SAMANN, self-organizing map (SOM), and locally linear  embedding (LLE). 7. Input selection using fuzzy curves and surfaces. 8. Genetic programming assisted stochastic process optimization strategies. 9. Optimization of continuous distillation columns using stochastic optimization approaches. 10.Genetic algorithms to optimize batch-distillation. 11.GA based optimization of glucose to gluconic acid fermentation. 12.Soft-sensors for biochemical processes using support vector regression 13.Modeling and optimization of bio-chemical processes using ANN-GA hybrid approach 14.ANN-based models for predicting gross calorific value of Indian coals Most recent 3-5 publications: 1. S. Nandi, S. Ghosh, S.S. Tambe, B.D. Kulkarni (2001) Artificial neural network assisted stochastic process optimization strategies, AIChE. J. 47(1), 126-141. 2. J.J.S. Cheema, N. V. Sankpal, S. S. Tambe and B. D. Kulkarni (2002), Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation, Biotech. Prog. 18(6), 1356-1365. 3. J. R. Kulkarni, Savita G. Kulkarni, Y. Badhe, S. S. Tambe, B. D. Kulkarni and G. B. Pant, "Multi-model scheme for prediction of monthly rainfall over India, " Research Report No. RR-101, ISSN 0252-1075, pp. 1-28, Indian Institute of Tropical Meteorology, Pune 411 008, India (December 2003). 4. Kiran Desai, Yogesh Badhe, Sanjeev S. Tambe and Bhaskar D. Kulkarni, Soft-sensor  Development for Fed Batch Bioreactors using Support Vector Regression, BioChemical Engineering Journal, Vol 27, Issue 3, 225-239 (2005). 5. S. Patel, B. Jeevan Kumar, Y. P. Badhe, S. Saha, S. Biswas, Asim Chaudhuri, B. K. Sharma, S. S. Tambe, and B. D. Kulkarni, "Estimation of Gross Calorific Value of Coals using Artificial  Neural Networks", Fuel , 86, 334-344 (2007). Some significant past projects executed: 1. Modeling of gas cracker  2. Soft-sensor development for industrial polyethylene manufacture 3. Design and Development of genetic and memetic programming software for data-based modeling 4. AI based modeling and optimization of flue-gas conditioning system. Any major achievements (restrict to 2-3): 1. An exclusively data-driven hybrid strategy (termed "ANN-GA") integrating artificial neural networks and genetic algorithms has been designed, developed and validated on several  processes. The major advantage of the strategy is that process optimization can be performed solely based on the historic process input-output data. 2. A strategy has been developed for constructing artificial neural network models possessing improved prediction accuracy and generalization performance in presence of data containing instrumental noise and measurement errors. US and European patents have been filed for the said invention.

- 38 -

National Chemical Laboratory, Pune 3. A linear/nonlinear data-driven modeling formalism termed as "memetic programming" has been designed, developed. and validated for a number of process modeling tasks. This formalism is capable of generating system-specific multiple input - single output (MISO) data-fitting functions. The formalism employs global and local search of the solution space and has been found to out-perform genetic programming based solutions.

- 39 -

Tata Institute of Fundamental Research

School of Technology and Computer Science Spoken Language Processing Information provided by: Samudravijaya K ([email protected]) Other senior members: J A. Sen ([email protected]), Nandini Bondale ([email protected]) Website (for more information): http://speech.tifr.res.in Major focus areas of the group: Human Language Technology Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Speech Recognition and Text-to-speech Systems for Indian Languages 2. Spoken and Hand-written Language Resources 3. Pen computing (Indian Languages) Most recent 3-5 publications: 1. Samudravijaya K, "Development of Multi-lingual Spoken Corpora of Indian Languages", In Lecture Notes in Artificial Intelligence, LNAI 4274, Eds. Huo et. al, 5th International Symposium, ISCSLP 2006, Singapore, 2006, Springer Verlag, Germany, pp. 792-801. 2. Samudravijaya K, “Variable Frame Size Analysis for Speech Recognition", Proc. of the Int. Conf. on Natural Language Processing (ICON-2004), Dec 19-22, Hyderabad, Eds. R.Sangal and S.M.Bendre, Allied Publishers Pvt. Ltd. New Delhi, pp. 237-244. 3. A . Sen, "A Text Analyzer for Bangla Text-to-Speech Synthesis", Proc., CODEC-2004, Kolkata, Jan. 2004. 4. Samudravijaya K, Sheetal Shah, Paritosh Pandya, "Computer Recognition ofTabla Bols", Proc. FRSM 2004, January 2004, Chidambaram, pp. 48-52. 5. Samudravijaya K, Aswin Chandrasekar and Garima Sinha, "Online Hindi Character  Recognition", In `Artificial Intelligence: Therory and Practice', Proc. of Int. Conf. on Knowledge Based Computer Systems, Eds. Sasikumar M, Jayprasad J Hegde and Kavitha M, Vikas Publishing House Pvt Ltd, New Delhi, December 2002, pp 445-454. Some significant past projects executed: 1. "VOICE - Voice Oriented Interactive Computing Environment", as part of Knowledge Based Computing Systems project sponsored by Dept. of Electronics, Govt. of India and U.N.D.P. 1988-1993. 2. Text Dependent, Independent and text prompted Speaker Verification System, as part of Ecommerce project (DIT, Govt. India), 2002-2005.

- 40 -

Tata Institute of Fundamental Research

Any major achievements (restrict to 2-3): 1. Development of Speaker Independent, Continuous, Hindi Speech Recogntion system. 2. Creation of segmented and phonemically labelled, multi-speaker, continuous Hindi speech database. 3. Implementation of Writer Independent, Hindi character recognition system.

- 41 -

Tata Consultancy Services Limited

TCS Mumbai Cognitive Systems Applications Group)

Research

Laboratory

(Applied

Technology

Information provided by : PVS Rao ([email protected]) Other senior members: Akhilesh Srivastava, ([email protected]), Arun Pande ([email protected]), Sunil Kopparapu, ([email protected]), Sumitra Das, Sathyanarayana srinivasan, Ambikesh Shukla, Meghna Maiti, Dipti Desai, Anoop Bal, Kalyan Godavarthy, Devanuj, Irfan Khan Major areas of focus in your group : Speech and Natural Language Processing Brief description of at most three projects in progress:  Please include any website for more details for each project  The Cognitive Systems Research Laboratory has been active in the areas of speech, script and natural language processing. In each of these areas, its work has been oriented both towards application areas from the point of view of customer interest as well as research areas of  contemporary interest worldwide. An indicative partial list of areas that the Lab has been working on are Robust speech recognition, Language modeling, Voice Crafting, Knowledge-based NLP and Question Answering, Translingual information retrieval and Information extraction from free text and offline to online script conversion. In the area of speech, novel approaches for robust recognition have been suggested and investigated. In particular, two approaches have been  proposed: (a) treating the entire normalized spectrogram as a 3-dimensional surface and attempting recognition by comparison of shapes as between the test sample and the trained model, and (b) using an idealized vocal tract based generative model to attempt recognition in the articulatory domain.

Unconventional approaches to language modeling have been tried out and successfully utilized for  development of grammar tools and communication aids in situations where the user has limited reading ability in the first language and no familiarity at all in the second language. A novel key-concept approach which dispenses with the need for parsing the question or the information  base. A flexible question answering system has been developed using the above approach. The flexibility of the system was demonstrated by applying it for a number of domains including the Tata Infotech website, textual and database information from the Sahara Airlines database, train and ticket information from the Indian Railways website, text book on management information systems and e-book on physical fitness and more recently to extract information from Mumbai and Delhi Yellow Pages databases. - 42 -

Tata Consultancy Services Limited Translingual information retrieval technology is currently being applied for querying agricultural information in Hindi. Several working systems have been implemented for extracting all the relevant information from tender advertisements appearing in the newspapers and from CVs of  applicants to various advertised posts. Most recent 3-5 publications: 1. Sunil Kopparapu, Sathyanarayana Srinivasan, Akhilesh Srivastava, PVS Rao, Classification of  Speaking Style, November 24-25, 41st CSI Annual Convention, CSI 2006, Science City, Kolkota. 2. Sunil Kopparapu, Akhilesh Srivastava, P.V.S. Rao, Accessing Style of Spoken Speech Oriential COCOSDA 2006, Malyasia, 9-11 December 2006. 3. Sunil Kopparapu, Devanuj, Akhilesh Srivastava, P.V.S. Rao, Knowledge Driven Offline to Online Script Conversion National Workshop on Artificial Intelligence, 2-3 July, C-DAC, Juhu, Mumbai, 2006. 4. Sunil Kopparapu, Lighting Design for Machine Vision Applications, Image and Vision Computing July 2006. 5. Sunil Kopparapu, Akhilesh Srivastava, PVS Rao, A minimal parsing QA system, HCI International 2007, PR of China, July 2007.COCOSDA 2006, Malyasia, 9-11 December 2006.

TCS Delhi iLab, Applied Artificial Intelligence Information provided by : C. Anantaram ([email protected]) Other senior members: Shefali Bhat ([email protected]), Hemant Jain ([email protected]) Website (for more information) : http://www.ilab-tcs.com/ Major focus areas of the group : Natural Language Interfaces (Natural Language Processing), Multimodal reasoning for data-intensive domains, Rule-based reasoning Brief description of at most three projects in progress:  Please include any website for more details for each project  1. Natural language interface to Menu-driven Business systems: The current focus is on building natural language interfaces to menu-driven business application systems. While menu-driven interfaces are a standard mechanism to interact with business systems, such interfaces can become rather cumbersome for users of large applications, especially for users who know the kind of data they want from the system, but do not know which menus to traverse in order to get the data. An architecture of a text-based natural language conversational interface, called Natas, has been developed for menu-driven systems.  Natas permits the user to carry out a dialog with the system in order to fetch relevant data and carry out various tasks of the system. The architecture uses semantic web based ontology of the domain, to aid in the retrieval of the relevant data and concepts from the system.

- 43 -

Tata Consultancy Services Limited 2. Multimodal reasoning for data-intensive domains Knowledge-based systems in data-intensive domains need to process voluminous data and require multiple reasoning mechanisms to work together to produce effective solutions. We have developed a framework in which multiple heterogeneous reasoning sources are integrated to build a cooperative problem-solving mechanism. Our reasoning framework integrates  pattern-based, rule-based, and case-based reasoning sources, through an extended blackboard architecture with semantic-web based representation. 3. Rule-based reasoning We are working on defining rules on object models in such a way that the object models can capture various rules in the domain during modeling itself. We have developed a framework for  specifying declarative rules on objects, attributes and associations in the object-model for a domain. Most recent 3-5 publications: 1. C. Anantaram (2007). A Framework to specify Declarative Rules on Objects, Attributes and Associations in the object model, to appear in the Journal of Object Technology, ETH Swiss Federal Institute of Technology, Jan 2007. 2. C. Anantaram and Shefali Bhat (2005). A text-based conversational interface for menu-driven applications, using semantic-web technology, TACTiCS, TCS Technical Architects' Conference '05, Tata Consultancy Services Limited, Hyderabad, December 2005. 3. Shefali Bhat, C. Anantaram, and Hemant Jain (2006). Enabling E-Mail as an Alternate Means of  Communication with Business Applications, TACTiCS06, TCS Technical Architects' Conference '06, Tata Consultancy Services Limited, Hyderabad, December 2006. 4. C. Anantaram (2005). Processing and Reasoning over large Text and Numeric data sets in C4I2 domain, Closed Door Seminar on Developing C4I2 Capabilities and Embedded Systems, Confederation of Indian Industry, New Delhi, June 2005. 5. Anantaram (2004). Designing a rule engine for handling business rules, TACTiCS, 1st TCS Technical Architects' Conference, Tata Consultancy Services Limited, Hyderabad, October  2004.

Technology Innovation Lab Information provided by : Hiranmay Ghosh ([email protected]) Website (for more information) : http://www.ilab-tcs.com/ Major focus areas of the group : Knowledge Representation Brief description of at most three projects in progress:  Please include any website for more details for each project  We take a holistic view of rich media applications. We view the different rich media components are integral data elements of an application. They need to be generated, processed, transported and disseminated in an application context, just like numbers and textual data in existing applications. Integration of diverse forms of media data seamlessly requires their semantic interpretation and

- 44 -

Tata Consultancy Services Limited semantic transcoding depending on application needs. We are working towards building such an environment. To distinguish it from the existing semantic web, which primarily addresses data in textual form, we call it the Semantic Multimedia Web .

- 45 -

 Area-wise Index of Research groups

AREA

REFERENCE PAGES

Agents

11, 21, 27

Case Based Reasoning

12

Cellular Automata

17

Character Recognition

9, 27, 40

Cognitive Modeling

14, 25

Cross Lingual Information Retrieval

7, 24, 27

Data Mining

7, 12, 17, 37

Document Image Processing

9, 27

Document Categorization

30

Evolutionary Algorithms

15

Expert Systems

7, 14

Information Extraction

30

Information Retrieval

30

Intelligent Image Processing

25

Knowledge Representation

12, 44

Language Technology

36

Machine learning

12, 15, 17, 37

Machine Translation

7, 24, 28, 36

 Natural Language Interfaces

43

 Natural Language Processing

7, 12, 14, 17, 21, 24, 25, 30, 42

 Neural Networks

12, 22

Optimization

11, 15

Planning and Scheduling

7, 12, 17, 25

Question Answering

36

Reasoning

14, 43

Robotics

6, 14, 21, 33

Search

11, 17

Soft Computing

7, 11, 14, 14, 17, 21, 37

Speech Processing

12, 21, 22, 31, 40, 42

Text Summarization

30

Vision

14, 27

- 46 -

Information Template for the Compilation

Please fill up the template below and mail it to [email protected] or to the address on the back  cover, for addition or modification of profile in the compilation. 1) Name of Institution: 2) Department to which your group belongs: 3) Your research group (if there is no explicit group, you can omit this question): 4) Name of the person in charge & his/her email id: 5) Other senior members in your group along with their e-mail: 6) Web site where more information on your work will be available: 7) Major areas of focus in your group: 8) Brief description of at most three projects in progress in your group (please include any web site for more detail for each project): 9) Most recent 3-5 publications from your group: 10) Some significant past projects executed by your group: 11) Any major achievements (restrict to 2-3):

- 47 -