Faculty/ Kamalakar Karlapalem
Kamalakar Karlapalem
Professor
MIHIR SHEKHAR - (Active)
mihir.shekhar@research.iiit.ac.in
GARIMA JINDAL - (Active)
garima.jindal@research.iiit.ac.in
POOJA KAUL - (Graduated)
pooja.kaul@research.iiit.ac.in
CHIKKA VEERA RAGHAVENDRA - (Graduated)
raghavendra.ch@research.iiit.ac.in
RAHUL T - (Graduated)
rahul.t@research.iiit.ac.in
ANURA MOHANIA- (Graduated)
anura.mohania@research.iiit.ac.in
PABITRA MOHAPATRA- (Graduated)
pabitra.mohapatra@research.iiit.ac.in
PRATHYUSH KUMAR KASYAP S- (Graduated)
prathyush.sambaturu@research.iiit.ac.in
PULKIT VERMA- (Graduated)
pulkit.verma@research.iiit.ac.in
Subhasis Chand- (Graduated)
subhasis.chand@research.iiit.ac.in
Abhay Rawat- (Graduated)
abhay.rawat@research.iiit.ac.in
K Shravya- (Graduated)
shravya.k@research.iiit.ac.in
Monica P- (Graduated)
monica.p@research.iiit.ac.in
Ayushman Panda- (Active)
ayushman.panda@research.iiit.ac.in
Bhumika Joshi- (Active)
bhumika.joshi@research.iiit.ac.in
AVINASH KUMAR- (Graduated)
avinash_k@students.iiit.ac.in
KARTEEK BABU JASTI- (Graduated)
karteek_bj@students.iiit.ac.in
ASEEM ANAND- (Graduated)
aseem.anand@research.iiit.ac.in
NEHA SOORMA- (Graduated)
neha.soorma@research.iiit.ac.in
GARIMA AHUJA- (Graduated)
garima.ahuja@research.iiit.ac.in
VIVEK HAMIRWASIA- (Graduated)
vivek.hamirwasia@research.iiit.ac.in
PALLAV TINNA- (Graduated)
pallav.tinna@research.iiit.ac.in
GARAPATI SRAVYA SRI- (Graduated)
garapati.sravyasri@research.iiit.ac.in
P YASHASWI- (Graduated)
p.yashaswi@research.iiit.ac.in
YARRABELLY NAVYA- (Graduated)
yarrabelly.navya@research.iiit.ac.in
AYUSH DATTA- (Graduated)
ayush.datta@research.iiit.ac.in
ADITYA SURESH MOTWANI- (Graduated)
aditya.motwani@research.iiit.ac.in
SYED JUNED ALI- (Graduated)
juned.ali@research.iiit.ac.in
NIKHIL TADIGOPPULA- (Graduated)
nikhil.tadigoppula@research.iiit.ac.in
Aman Kamlesh Mehta- (Graduated)
aman.mehta@research.iiit.ac.in
Aashay Singhal- (Graduated)
aashay.singhal@research.iiit.ac.in
AKSHAT TANDON- (Graduated)
akshat.tandon@research.iiit.ac.in
Sirineni Yahnit- (Graduated)
sirineni.yahnit@research.iiit.ac.in
C Jayitha- (Graduated)
jayitha.c@research.iiit.ac.in
Nikhil Chandak- (Graduated)
nikhil.chandak@research.iiit.ac.in
SHIVUDU BHUVANAGIRI- (Graduated)
shivudu@students.iiit.ac.in
Abhishek Sharma- (Graduated)
abhishek.sharma@students.iiit.ac.in
PULLE SRI SATYA SRAVYA- (Graduated)
sssravya.pulle@students.iiit.ac.in
MUDDASANI VISHNUTEJ REDDY- (Graduated)
vishnutejreddy.m@students.iiit.ac.in
Areas of Expertise
Multi Agent and Robot Systems, Data Visualization, Text Analytics, Workflow Systems, Conceptual Modelling, Data Systems, Data Analytics
Selected Publications
Coverage Path Planning using Multiple AUVs with Nadir Gap
Nikhil Chandak,Charu Sharma,Kamalakar Karlapalem
Autonomous Robots and Multirobot Systems Workshop, ARMS-W, 2024
@inproceedings{bib_Cove_2024, AUTHOR = {Nikhil Chandak, Charu Sharma, Kamalakar Karlapalem}, TITLE = {Coverage Path Planning using Multiple AUVs with Nadir Gap}, BOOKTITLE = {Autonomous Robots and Multirobot Systems Workshop}. YEAR = {2024}}
Autonomous Underwater Vehicles (AUVs) play a vital role in explor- ing and mapping underwater environments. However, the presence of nadir gaps, or blind zones, in commercial AUVs can lead to unex- plored areas during mission execution, limiting their effectiveness. Our work addresses the challenges of path planning in the presence of nadir gaps and presents scalable coverage strategies for AUVs minimizing either the mission completion time or the total number of turns performed while ensuring complete exploration, eliminating the risk of leaving critical areas unexplored. We provide provably complete strategies and perform extensive simulations on diverse input configurations based on real-world instances to demonstrate the efficacy of our strategies.
CentralityViz: Comprehending Node-Centrality in Large Networks
GARIMA JINDAL,Kamalakar Karlapalem
International Symposium on Graph Drawing and Network Visualization, ISGDNV, 2024
@inproceedings{bib_Cent_2024, AUTHOR = {GARIMA JINDAL, Kamalakar Karlapalem}, TITLE = {CentralityViz: Comprehending Node-Centrality in Large Networks}, BOOKTITLE = {International Symposium on Graph Drawing and Network Visualization}. YEAR = {2024}}
A system to visualise large graphs
System and method for visualizing large graph data using spirals
GARIMA JINDAL,Kamalakar Karlapalem
United States Patent, Us patent, 2024
@inproceedings{bib_Syst_2024, AUTHOR = {GARIMA JINDAL, Kamalakar Karlapalem}, TITLE = {System and method for visualizing large graph data using spirals}, BOOKTITLE = {United States Patent}. YEAR = {2024}}
A system and method for visualizing large graph data using spirals are provided. The method includes (i) generating communities from an input graph data, (ii) configuring nodes in the communities in an ascending order based on a centrality measure, (iii) determining a spiral for the communities by configuring (a) a node with a highest centrality measure at a spiral center and (b) the nodes in a spiral shape based on the ascending order of the centrality measure along the spiral shape, (iv) determining a first super edge for the spiral of the communities, (v) generating a coarse graph using super nodes, the first super edge, and a second super edge, (vi) configuring, using a standard force-directed layout algorithm, corresponding spirals of the communities on the generated coarse graph based on a weight of the second super edge and attractive forces to generate the visualization of the large graph data.
LLM Driven Web Profile Extraction for Identical Names
Prateek Sancheti,Kamalakar Karlapalem,Kavita Vemuri
Companion Proceedings of the ACM Web Conference, WWW- companion, 2024
@inproceedings{bib_LLM__2024, AUTHOR = {Prateek Sancheti, Kamalakar Karlapalem, Kavita Vemuri}, TITLE = {LLM Driven Web Profile Extraction for Identical Names}, BOOKTITLE = {Companion Proceedings of the ACM Web Conference}. YEAR = {2024}}
The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.
A Simple yet Useful Spiral Visualization of Large Graphs
GARIMA JINDAL,Kamalakar Karlapalem
IEEE Visualization (VAST, INFOVIS, SCIVIS), VIS, 2023
@inproceedings{bib_A_Si_2023, AUTHOR = {GARIMA JINDAL, Kamalakar Karlapalem}, TITLE = {A Simple yet Useful Spiral Visualization of Large Graphs}, BOOKTITLE = {IEEE Visualization (VAST, INFOVIS, SCIVIS)}. YEAR = {2023}}
We present a Spiral Visualization that facilitates users to visually comprehend large graphs. Spiral Visualization is a representation that highlights key aspects of networks, including the number, size, and density of communities, important or central nodes within communities, centrality distribution within communities, connections between communities, and connections between nodes. To facilitate analysis and comprehension of networks using various interaction techniques, such as zooming, tooltip, and highlight, we have implemented a Spiral Visualization dashboard. We conducted a qualitative user study incorporating observation, think-aloud protocols, and participant rating of confidence and easiness to assess the usability and suitability of our visualization. The findings suggest that our visualization is appropriate for the network tasks evaluated. However, tasks requiring color comparison, such as identifying the densest community and comparing community densities were found to be more challenging to perform.
Aspect-based Summarization of Legal Case Files using Sentence Classification
E Nikhil,Anshul Padhi,PARIKH PULKIT TRUSHANT KUMAR,Swati Kanwal,Kamalakar Karlapalem,Natraj Raman
Companion Proceedings of the ACM Web Conference, WWW- companion, 2023
@inproceedings{bib_Aspe_2023, AUTHOR = {E Nikhil, Anshul Padhi, PARIKH PULKIT TRUSHANT KUMAR, Swati Kanwal, Kamalakar Karlapalem, Natraj Raman}, TITLE = {Aspect-based Summarization of Legal Case Files using Sentence Classification}, BOOKTITLE = {Companion Proceedings of the ACM Web Conference}. YEAR = {2023}}
Aspect-based summarization of a legal case file related to regulating bodies allows different stakeholders to consume information of interest therein efficiently. In this paper, we propose a multi-step process to achieve the same. First, we explore the semantic sentence segmentation of SEBI case files via classification. We also propose a dataset of Indian legal adjudicating orders which contain tags from carefully crafted domain-specific sentence categories with the help of legal experts. We experiment with various machine learning and deep learning methods for this multi-class classification. Then, we examine the performance of numerous summarization methods on the segmented document to generate persona-specific summaries. Finally, we develop a pipeline making use of the best methods in both sub-tasks to achieve high recall.
Scenic Routes in Rd
Kamalakar Karlapalem
Technical Report, arXiv, 2023
@inproceedings{bib_Scen_2023, AUTHOR = {Kamalakar Karlapalem}, TITLE = {Scenic Routes in Rd}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
In this work, we introduce the problem of scenic routes among points in R d . The key development is the nature of the problem in terms of both defining the concept of scenic points and scenic routes and then coming up with algorithms that meet different criteria for the generated scenic routes. The scenic routes problem provides a visual trajectory for a user to comprehend the layout of high-dimensional points. The nature of this trajectory and the visual layout of the points have applications in comprehending the results of machine learning supervised and unsupervised learning techniques. We study the problem in 2D and 3D (with two color points) before exploring the issues in R d . The red/blue points in our examples could be to be in a class or not to be in a class. The applications could include landscape design to adhere to the scenic beauty of the artifacts on the ground. The generation of equally separated layouts for designing composite hardware where interference could be an issue. Keywords: sce
Capturing Hiders with Moving Obstacles
Ayushman Panda,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2023
@inproceedings{bib_Capt_2023, AUTHOR = {Ayushman Panda, Kamalakar Karlapalem}, TITLE = {Capturing Hiders with Moving Obstacles}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2023}}
The classic hide-and-seek game is an abstraction for many real- world scenarios like capturing intruders in a closed space, locating objects, patrolling an area, etc. Since most of the present work is based on static obstacles, we address solutions for the hide-and- seek game in an environment where the obstacles are not static. We design strategies that would facilitate seekers to capture hiders in an environment with moving obstacles. We have three strategies: Baseline strategy, Set-cover strategy, and Sweep strategy, which use different surveillance techniques to be followed by the seekers. We simulate the methods and compare their performance in different scenarios. While the baseline strategy demands many seekers in large environments, the other two strategies, set-cover and sweep, are ideal for applying in large environments as they require fewer seekers in the same environment.
Scenic Routes with Weighted Points in 2D
Vijayraj Shanmugaraj,Lini Teresa Thomas,Kamalakar Karlapalem
Technical Report, arXiv, 2023
@inproceedings{bib_Scen_2023, AUTHOR = {Vijayraj Shanmugaraj, Lini Teresa Thomas, Kamalakar Karlapalem}, TITLE = {Scenic Routes with Weighted Points in 2D}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
In a given 2D space, we can have points with different levels of importance. One would prefer viewing those points from a closer/farther position per their level of importance. A point in 2D from where the user can view two given points per his/her preference of distance is termed a scenic point. We develop the concept of scenic paths in a 2D space for two points that have weights associated with them. Subsequently, we propose algorithms to generate scenic routes a traveler can take, which cater to certain principles which define the scenic routes. Following are the contributions of this paper: (1) mathematical formulation of a scenic point, (2) introduction of scenic routes formed by such scenic points in two-class point configurations in 2D spaces, and (3) design of scenic route generation algorithms that fulfill certain defined requirements.
Scenic Routes over Points in 2D Space
Loay Rashid,Lini Teresa Thomas,Kamalakar Karlapalem
Technical Report, arXiv, 2023
@inproceedings{bib_Scen_2023, AUTHOR = {Loay Rashid, Lini Teresa Thomas, Kamalakar Karlapalem}, TITLE = {Scenic Routes over Points in 2D Space}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
Consider a 2D coordinate space with a set of red and a set of blue points. We define a scenic point as a point that is equidistant to a red point and a blue point. The set of contiguous scenic points form a scenic path. The perpendicular bisectors to the line joining a red point and a blue point forms a scenic path between the red point and the blue point. A scenic route is a traversal made from scenic paths. In this paper, we address this novel problem by (i) designing algorithms for scenic route generation, (ii) studying the algorithms different properties and (iii) analyzing the routes generated by these algorithms. Scenic routes have applications in geo-spatial visualizations and visual analytics
Analysis of h-index for research awards
Aashay Singhal,Kamalakar Karlapalem
Technical Report, arXiv, 2023
@inproceedings{bib_Anal_2023, AUTHOR = {Aashay Singhal, Kamalakar Karlapalem}, TITLE = {Analysis of h-index for research awards}, BOOKTITLE = {Technical Report}. YEAR = {2023}}
In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute h- index of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. We also compare h-index of a paper with nine h-index like metrics. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.
System and method for generating a table-driven mutable blockchain
Kamalakar Karlapalem,Suyash Kandele
United States Patent, Us patent, 2023
@inproceedings{bib_Syst_2023, AUTHOR = {Kamalakar Karlapalem, Suyash Kandele}, TITLE = {System and method for generating a table-driven mutable blockchain}, BOOKTITLE = {United States Patent}. YEAR = {2023}}
A system and method for generating a table-driven mutable blockchain are provided. The system includes one or more users 102A-102N associated with one or more nodes 104A-104N, a distributed file unit 106, a distributed ledger 108. The distributed file unit 106 stores a Lineage Table 200 and a global variable P 112, to keep count on the total number of blockchains in the system. The distributed ledger 108 stores one or more blockchains. The node 104A receives transaction details of transaction provided by user 102A through network 110, and defines transaction. The node 104 initializes linked list structure and creates a transaction of which a plurality of modifications is performed on it. The node 104 creates modified transaction in subsequent blockchains and then links these versions by adding sequence of elements in Linked List. The node 104 authenticates new transactions in main blockchain and the modified transactions in subsequent blockchains.
hp-frac: An index to determine Awarded Researchers
Aashay Singhal,Kamalakar Karlapalem
International Conference on World wide web - workshop, WWW-W, 2023
@inproceedings{bib_hp-f_2023, AUTHOR = {Aashay Singhal, Kamalakar Karlapalem}, TITLE = {hp-frac: An index to determine Awarded Researchers}, BOOKTITLE = {International Conference on World wide web - workshop}. YEAR = {2023}}
In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute hindex of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.
Missing Data Resilient Ensemble Subspace Decision Tree Classifier
Sairam U,Radha Krishna P,Kamalakar Karlapalem
Joint International Conference on Data Science & Management of Data, CODS-COMAD, 2023
@inproceedings{bib_Miss_2023, AUTHOR = {Sairam U, Radha Krishna P, Kamalakar Karlapalem}, TITLE = {Missing Data Resilient Ensemble Subspace Decision Tree Classifier}, BOOKTITLE = {Joint International Conference on Data Science & Management of Data}. YEAR = {2023}}
The construction of a decision tree on a given data set involves certain features. In case these features have NULL values during the production environment, the decision tree performance suffers. In this work, we develop a methodology to generate subspace deci- sion tree classifiers, where each decision tree uses non-overlapping subsets of feature sets. We build an ensemble classifier over these subspace classifiers to get better performance. We consider different levels of missing data in our ensemble classifier, to show that it is resilient to missing data and can also give better performance even in cases of incomplete data (that is, data with missing values). We show the viability of our methodology using real-life and synthetic data sets. Our approach delivers a simple yet effective missing data resilient decision tree classifier
Biomedical NER using Novel Schema and Distant Supervision
Anshita Khandelwal,Alok Kumar Kar,CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
Biomedical Language Processing, BioNLP, 2022
@inproceedings{bib_Biom_2022, AUTHOR = {Anshita Khandelwal, Alok Kumar Kar, CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {Biomedical NER using Novel Schema and Distant Supervision}, BOOKTITLE = {Biomedical Language Processing}. YEAR = {2022}}
Biomedical Named Entity Recognition (BMNER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets. In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model. For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model. We also achieve higher F1- scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.
SEBI Regulation Biography
Buggana Sathvik Sanjeev,Deepti Saravanan,K Shravya,Ujwal Narayan N,Shivam Sadashiv Mangale,Lini Teresa Thomas,Kamalakar Karlapalem,Natraj Raman
Web Conference, WC, 2022
@inproceedings{bib_SEBI_2022, AUTHOR = {Buggana Sathvik Sanjeev, Deepti Saravanan, K Shravya, Ujwal Narayan N, Shivam Sadashiv Mangale, Lini Teresa Thomas, Kamalakar Karlapalem, Natraj Raman}, TITLE = {SEBI Regulation Biography}, BOOKTITLE = {Web Conference}. YEAR = {2022}}
The Securities and Exchange Board of India is the regulatory body for securities and commodity market in India. A growing number of SEBI documents ranging from government regulations to legal case files are now available in the digital form. Advances in natural language processing and machine learning provide opportunities for extracting semantic insights from these documents. We present here a system that performs semantic processing of SEBI documents using state-of-the-art language models to produce enriched regulations containing timelines of amendments and cross references to legal case files.
Detecting Regulation Violations for an Indian Regulatory body through multilabel classification
Ujwal Narayan N,PARIKH PULKIT TRUSHANT KUMAR,Kamalakar Karlapalem,Natraj Raman
Web Conference, WC, 2022
@inproceedings{bib_Dete_2022, AUTHOR = {Ujwal Narayan N, PARIKH PULKIT TRUSHANT KUMAR, Kamalakar Karlapalem, Natraj Raman}, TITLE = {Detecting Regulation Violations for an Indian Regulatory body through multilabel classification}, BOOKTITLE = {Web Conference}. YEAR = {2022}}
The Securities and Exchange Board of India (SEBI) is the regulatory body for securities and commodities in India. SEBI creates, and enforces regulations that must be followed by all listed companies. To the best of our knowledge, this is the first work on identifying the regulation (s) that a SEBI-related case violates, which could be of substantial value to companies, lawyers, and other stakeholders in the regulatory process. We create a dataset for this task by automatically extracting violations from publicly available case-files. Using this data, we explore various multi-label text classification methods to determine the potentially multiple regulations violated by (the facts of) a case. Our experiments demonstrate the importance of employing contextual text representations to understand complex financial and legal concepts. We also highlight the challenges that must be addressed to develop a fully functional system in the real-world.
A Multi Perspective Access Control in a Smart Home
K Shravya,Kamalakar Karlapalem
ACM Conference on Data and Application Security and Privacy, CODASPY, 2021
@inproceedings{bib_A_Mu_2021, AUTHOR = {K Shravya, Kamalakar Karlapalem}, TITLE = {A Multi Perspective Access Control in a Smart Home}, BOOKTITLE = {ACM Conference on Data and Application Security and Privacy}. YEAR = {2021}}
Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device, (ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys [8] on building a comprehensive access control system to manage privileges in a shared smart home. CCS CONCEPTS
A Multi Perspective Access Control in a Smart Home
K Shravya,Kamalakar Karlapalem
ACM Conference on Data and Application Security and Privacy, CODASPY, 2021
@inproceedings{bib_A_Mu_2021, AUTHOR = {K Shravya, Kamalakar Karlapalem}, TITLE = {A Multi Perspective Access Control in a Smart Home}, BOOKTITLE = {ACM Conference on Data and Application Security and Privacy}. YEAR = {2021}}
Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device,(ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys on building a comprehensive access control system to manage privileges in a shared smart home.
Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems
Yahnit Sirineni,Rahul Tallamraju, Abhay Rawat,Kamalakar Karlapalem
International Conference on Safety, Security, and Rescue Robotics, SSRR, 2020
@inproceedings{bib_Dece_2020, AUTHOR = {Yahnit Sirineni, Rahul Tallamraju, Abhay Rawat, Kamalakar Karlapalem}, TITLE = {Decentralized Collision Avoidance and Motion Planning for Multi-Robot Deformable Payload Transport Systems}, BOOKTITLE = {International Conference on Safety, Security, and Rescue Robotics}. YEAR = {2020}}
We present a decentralized motion planning and collision avoidance algorithm for multi-robot payload transport systems (PTS). A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a deformable payload. Each PTS is constrained to navigate safely in a dynamic environment by inter-formation, environmental, and intra-formation collision avoidance. Real-time collision avoidance for such systems is challenging due to the deformability of formations and high dimensional multi-robot non-convex workspace. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. Specifically, we present two main steps to plan the motion of each formation. First, we compute collision-free multi-scale convex workspaces over a planning
Conformance Checking Methodology Across Discharge Summaries and Standard Treatment Guidelines
CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
Transactions on Computing for Healthcare, TCH, 2020
@inproceedings{bib_Conf_2020, AUTHOR = {CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {Conformance Checking Methodology Across Discharge Summaries and Standard Treatment Guidelines}, BOOKTITLE = {Transactions on Computing for Healthcare}. YEAR = {2020}}
Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease …
Multi-Robot Formation Control Using Reinforcement Learning
Abhay Rawat,Kamalakar Karlapalem
Technical Report, arXiv, 2020
@inproceedings{bib_Mult_2020, AUTHOR = {Abhay Rawat, Kamalakar Karlapalem}, TITLE = {Multi-Robot Formation Control Using Reinforcement Learning}, BOOKTITLE = {Technical Report}. YEAR = {2020}}
In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.
Capturing Oracle Guided Hiders
AKSHAT TANDON,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2020
@inproceedings{bib_Capt_2020, AUTHOR = {AKSHAT TANDON, Kamalakar Karlapalem}, TITLE = {Capturing Oracle Guided Hiders}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2020}}
Consider a closed environment with static obstacles and mobile agents moving around. There are hider agents that hide from the seeker agents. The seeker has a limited visibility range, and if a hider comes into the visibility region of a seeker, it is considered caught. The practical applications range from gaming to security. In this work, we focus on deterministic capture of hiders, even if they are guided by an Oracle which knows the future positions of seekers. We develop strategies for seekers, having limited visibility ranges, to catch all hiders and establish minimum bounds on the number of seekers required to catch the hiders, on a per strategy basis. We use spatio-temporal graph models and reasoning to formulate and address the problem.
Conformance Checking Methodology Across Discharge Summaries and Standard Treatment Guidelines
CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
Transactions on Computing for Healthcare, TCH, 2020
@inproceedings{bib_Conf_2020, AUTHOR = {CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {Conformance Checking Methodology Across Discharge Summaries and Standard Treatment Guidelines}, BOOKTITLE = {Transactions on Computing for Healthcare}. YEAR = {2020}}
Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease, these guidelines are curated, aggregated, and represented as a workflow specification. We commend multiple measures to compute the conformance of workflow instance with workflow specification. We validate our conformance checking methodology using discharge summary data of three diseases, namely colon cancer, coronary artery disease, and brain tumor, collected from THYME corpus and MIMIC III clinical database. Our approach and the solution can be used by hospitals and patients to determine adherence, gaps, and additions to standard treatment plans. Further, our work can facilitate to identify common errors and goodness in actual enactment of treatment plans, which can further lead to refinement of standard treatment plans.
A Data Logistics System for Internet of Things
SYED JUNED ALI,Radha Krishna Pisipati,Kamalakar Karlapalem
IEEE World Congress on Services, SERVICES, 2019
@inproceedings{bib_A_Da_2019, AUTHOR = {SYED JUNED ALI, Radha Krishna Pisipati, Kamalakar Karlapalem}, TITLE = {A Data Logistics System for Internet of Things}, BOOKTITLE = {IEEE World Congress on Services}. YEAR = {2019}}
In IoT applications, sensors produce raw data, and smart solutions consume the processed data. IoT driven smart solutions typically use data that is aggregated, filtered and processed to drive the smart solution. Such aggregated data has different semantics in comparison to raw sensor data, and it needs to be customized simultaneously for different application requirements. There is a need for proper data logistics for the movement of data from the sensors producing raw data to smart solutions consuming solution relevant data. In this paper, we present a data logistics system that includes (i) architecture to support the movement of data from sensors to smart applications, and (ii) event-driven solution to trigger workflows of smart solution. Workflows in our solution supports the data fulfillment requirements for the enactment of smart solutions
Extracting Evidence Summaries from Detective Novels
ADITYA SURESH MOTWANI,AAYUSH NAIK,Kamalakar Karlapalem
European Conference on Information Retrieval, ECIR, 2019
@inproceedings{bib_Extr_2019, AUTHOR = {ADITYA SURESH MOTWANI, AAYUSH NAIK, Kamalakar Karlapalem}, TITLE = {Extracting Evidence Summaries from Detective Novels}, BOOKTITLE = {European Conference on Information Retrieval}. YEAR = {2019}}
Literary novels are a mine of information stored in a narrative. Information Extraction (IE) on these novels can be directed by a specific aim to derive knowledge of interest. In this paper, we explore the problem of extracting evidence summaries for all characters in a detective novel. But in this domain, there is no standard annotated text which provides the ground truth for evidence and there exists no fixed metric of evaluation. For extracting summaries, we explore unsupervised methods of learning. We first assume that all characters are equally likely to be culprits and build evidence against each character. Given this evidence, we assess the likelihood of each character being the culprit, eventually arriving at the true culprit(s). Our contribution is the formulation of evidence summaries for characters in determining the culprit. Our experiments are based on a corpus of detective novels spanning the last hundred years. We evaluate these summaries for comprehension with the help of human readers and broadly categorize novels into three groups.
Loosely Coupled Payload Transport System with Robot Replacement
PULKIT VERMA,RAHUL T,Abhay Rawat,Subhasis Chand,Kamalakar Karlapalem
Technical Report, arXiv, 2019
@inproceedings{bib_Loos_2019, AUTHOR = {PULKIT VERMA, RAHUL T, Abhay Rawat, Subhasis Chand, Kamalakar Karlapalem}, TITLE = {Loosely Coupled Payload Transport System with Robot Replacement}, BOOKTITLE = {Technical Report}. YEAR = {2019}}
In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of non-holonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.
Scalable Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping
Aman Kamlesh Mehta,Aashay Singhal,Kamalakar Karlapalem
International Conference on World wide web - workshop, WWW-W, 2019
@inproceedings{bib_Scal_2019, AUTHOR = {Aman Kamlesh Mehta, Aashay Singhal, Kamalakar Karlapalem}, TITLE = {Scalable Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping}, BOOKTITLE = {International Conference on World wide web - workshop}. YEAR = {2019}}
Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.
MULTIPLE DRONES DRIVEN HEXAGONALLY PARTITIONED AREA EXPLORATION: SIMULATION AND EVALUATION
AYUSH DATTA,RAHUL T,Kamalakar Karlapalem
Summer Simulation Conference, SCSC, 2019
@inproceedings{bib_MULT_2019, AUTHOR = {AYUSH DATTA, RAHUL T, Kamalakar Karlapalem}, TITLE = {MULTIPLE DRONES DRIVEN HEXAGONALLY PARTITIONED AREA EXPLORATION: SIMULATION AND EVALUATION}, BOOKTITLE = {Summer Simulation Conference}. YEAR = {2019}}
In this paper, we simulated a distributed, cooperative path planning technique for multiple drones (∼200) to explore an unknown region (∼10,000 connected units) in the presence of obstacles. The map of an unknown region is dynamically created based on the information obtained from sensors and other drones. The unknown area is considered a connected region made up of hexagonal unit cells. These cells are grouped to form larger cells called sub-areas. We use long range and short range communication. The short-range communication within drones in smaller proximity helps avoid re-exploration of cells already explored by companion drones located in the same subarea. The long-range communication helps drones identify next subarea to be targeted based on weighted RNN (Reverse nearest neighbor). Simulation results show that weighted RNN in a hexagonal representation makes exploration more efficient, scalable and resilient to communication failures
Active Perception Based Formation Control for Multiple Aerial Vehicles
RAHUL T,Eric Price,Roman Ludwig,Kamalakar Karlapalem,Heinrich H. Bülthoff,Michael J. Black,Aamir Ahmad
IEEE Robotics and Automation Letters, RAL, 2019
@inproceedings{bib_Acti_2019, AUTHOR = {RAHUL T, Eric Price, Roman Ludwig, Kamalakar Karlapalem, Heinrich H. Bülthoff, Michael J. Black, Aamir Ahmad}, TITLE = {Active Perception Based Formation Control for Multiple Aerial Vehicles}, BOOKTITLE = {IEEE Robotics and Automation Letters}. YEAR = {2019}}
We present a novel robotic front-end for autonomous aerial motion-capture (mocap) in outdoor environments. In previous work, we presented an approach for cooperative detection and tracking (CDT) of a subject using multiple micro-aerial vehicles (MAVs). However, it did not ensure optimal view-point configurations of the MAVs to minimize the uncertainty in the person’s cooperatively tracked 3D position estimate. In this article, we introduce an active approach for CDT. In contrast to cooperatively tracking only the 3D positions of the person, the MAVs can actively compute optimal local motion plans, resulting in optimal view-point configurations, which minimize the uncertainty in the tracked estimate. We achieve this by decoupling the goal of active tracking into a quadratic objective and non-convex constraints corresponding to angular configurations of the MAVs w.r.t. the person. We derive this decoupling using Gaussian observation model assumptions within the CDT algorithm. We preserve convexity in optimization by embedding all the non-convex constraints, including those for dynamic obstacle avoidance, as external control inputs in the MPC dynamics. Multiple real robot experiments and comparisons involving 3 MAVs in several challenging scenarios are presented.
Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems
RAHUL T,Durgesh Haribhau Salunkhe,Sujit Rajappa,Aamir Ahmad,Kamalakar Karlapalem,Max Planck Institute for Intelligent Systems, Tubingen, Germany
International Conference on Automation Science and Engineering, ICASE, 2019
@inproceedings{bib_Moti_2019, AUTHOR = {RAHUL T, Durgesh Haribhau Salunkhe, Sujit Rajappa, Aamir Ahmad, Kamalakar Karlapalem, Max Planck Institute For Intelligent Systems, Tubingen, Germany}, TITLE = {Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2019}}
In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute realtime collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. This approach facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments.
Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems
Sirineni Yahnit,PULKIT VERMA,Kamalakar Karlapalem
International Symposium on Multi-Robot and Multi-Agent Systems, MRS, 2019
@inproceedings{bib_Traf_2019, AUTHOR = {Sirineni Yahnit, PULKIT VERMA, Kamalakar Karlapalem}, TITLE = {Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems}, BOOKTITLE = {International Symposium on Multi-Robot and Multi-Agent Systems}. YEAR = {2019}}
In this work, we address traffic management of multiple payload transport systems comprising of nonholonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each PTS completes its given task by avoiding collisions with other payload systems and obstacles as well. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle with respect to the leader using a decentralized leader follower control architecture while moving in the traffic. We showcase, through simulations the time taken by each PTS to traverse its respective trajectory with and without other PTS and obstacles. We show that our strategies help manage the traffic for a large number of PTS moving from one place to another.
Transportation of Deformable Payload through Static and Dynamic Obstacles using Loosely Coupled Nonholonomic Robots
Subhasis Chand,PULKIT VERMA,RAHUL T,Kamalakar Karlapalem
International Symposium on Multi-Robot and Multi-Agent Systems, MRS, 2019
@inproceedings{bib_Tran_2019, AUTHOR = {Subhasis Chand, PULKIT VERMA, RAHUL T, Kamalakar Karlapalem}, TITLE = {Transportation of Deformable Payload through Static and Dynamic Obstacles using Loosely Coupled Nonholonomic Robots}, BOOKTITLE = {International Symposium on Multi-Robot and Multi-Agent Systems}. YEAR = {2019}}
This paper presents our solution for transportation of deformable payloads using a formation of non-holonomic mobile robots while avoiding static and dynamic obstacles. We assume the center of the formation to be a virtual robot which also plays the role of the leader of the formation. Rapidly exploring Random Tree star (RRT*) algorithm is used to determine a static obstacle free trajectory for the virtual leader. In addition to that, a custom path planning algorithm is incorporated for the follower robots. A decentralized leader-follower formation control is applied for the robots to traverse their respective trajectories without breaking the formation while following the constraints of the deformable payload. The virtual leader moves forward or backward on the initially generated path with variable velocity depending on the proximity of dynamic obstacles. All the other robots follow it so that the formation is able to avoid the dynamic obstacles while still remaining on the path leading towards the destination.
FAST : Fragment Assisted Storage for efficient query execution in read-only databases
VIVEK HAMIRWASIA,Kamalakar Karlapalem,Satyanarayana R Valluri
Real-Time Business Intelligence and Analytics, BIRTE, 2019
@inproceedings{bib_FAST_2019, AUTHOR = {VIVEK HAMIRWASIA, Kamalakar Karlapalem, Satyanarayana R Valluri}, TITLE = {FAST : Fragment Assisted Storage for efficient query execution in read-only databases}, BOOKTITLE = {Real-Time Business Intelligence and Analytics}. YEAR = {2019}}
Traditional row store has the disadvantage of reading irrelevant attributes into main memory, when only a few attributes are queried and it incurs a large number of cache misses. Column store, while being cache efficient, suffers from large “stitching” costs if the number of attributes queried are large. In this paper, we introduce FAST, an intuitive, hybrid combination of both these storage mechanisms that leverages the main memory to reduce the I/O time and cache misses to efficiently serve ad-hoc read-only queries in real time. Our system acts as a commercial off-the-shelf (COTS) solution on top of existing databases. We show that on average, FAST executes queries up-to an order of magnitude faster than row storage and as much as twice as fast than column storage for ad-hoc data warehouse workloads. Our results show superior performance of FAST on multiple TPC-H queries.We also present techniques to automatically adapt the main memory layout to a changing workload.
Energy Conscious Over-actuated Multi-Agent Payload Transport Robot: Simulations and Preliminary Physical Validation
RAHUL T,PULKIT VERMA,Venkatesh Sripada,Shrey Agrawal,Kamalakar Karlapalem
IEEE International Conference on Robot and Human Interactive Communication, RO-MAN, 2019
@inproceedings{bib_Ener_2019, AUTHOR = {RAHUL T, PULKIT VERMA, Venkatesh Sripada, Shrey Agrawal, Kamalakar Karlapalem}, TITLE = {Energy Conscious Over-actuated Multi-Agent Payload Transport Robot: Simulations and Preliminary Physical Validation}, BOOKTITLE = {IEEE International Conference on Robot and Human Interactive Communication}. YEAR = {2019}}
In this work, we consider a multi-wheeled payload transport system. Each of the wheels can be selectively actuated.When they are not actuated, wheels are free moving and do not consume battery power. The payload transport system is modeled as an actuated multi-agent system, with each wheelmotor pair as an agent. Kinematic and dynamic models are developed to ensure that the payload transport system moves as desired. We design optimization formulations to decide on the number of wheels to be active and which of the wheels to be active so that the battery is conserved and the wear on the motors is reduced. Our multi-level control framework over the agents ensures that near-optimal number of agents is active for the payload transport system to function. Through simulation studies we show that our solution ensures energy efficient operation and increases the distance traveled by the payload transport system, for the same battery power. We have built the payload transport system and provide results for preliminary experimental validation.
Agent Strategies for the Hide-and-Seek Game
AKSHAT TANDON,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2018
@inproceedings{bib_Agen_2018, AUTHOR = {AKSHAT TANDON, Kamalakar Karlapalem}, TITLE = {Agent Strategies for the Hide-and-Seek Game}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2018}}
We are given an environment with some objects (a city block area) and mobile agents moving in the environment. An agent (hider) can hide behind an object to be not seen by other agents (seekers) through their line of sight (visibility). The aim of hiders is not to be caught for the longest time, and the aim of the seekers is catch all of them in the shortest period of time. We formulate the problem by using visibility based map abstractions. Agents plan their moves by utilizing multi-armed bandits UCB reward update model. We evaluate our abstractions and strategies by simulating the game under various different scenarios.
Estimating Credibility of News Authors from their WIKI Validated Predictions
YARRABELLY NAVYA,Kamalakar Karlapalem
European Conference on Information Retrieval, ECIR, 2018
@inproceedings{bib_Esti_2018, AUTHOR = {YARRABELLY NAVYA, Kamalakar Karlapalem}, TITLE = {Estimating Credibility of News Authors from their WIKI Validated Predictions}, BOOKTITLE = {European Conference on Information Retrieval}. YEAR = {2018}}
In this paper, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we approach is determining the credibility of the authors based on the predictions coming out to be true. The two specific problems we address are extracting the predictions from the articles and annotating with various prediction attributes. And then we determine the truth of these predictions, using Wikipedia as a credible source to extract relevant facts which can ascertain the validity of the predictions. We proposed and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We considered 28 news articles and extracted 97 predictions from these articles and the range of credibility scores(Fscores) for these articles are (0.57-0.71).
Extracting Predictive Statements with Their Scope from News Articles
YARRABELLY NAVYA,Kamalakar Karlapalem
International Conference on Web and Social Media, ICWSM, 2018
@inproceedings{bib_Extr_2018, AUTHOR = {YARRABELLY NAVYA, Kamalakar Karlapalem}, TITLE = {Extracting Predictive Statements with Their Scope from News Articles}, BOOKTITLE = {International Conference on Web and Social Media}. YEAR = {2018}}
We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting futurerelated information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.
A hybrid deep learning approach for medical relation extraction
CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOPS, KDD-W, 2018
@inproceedings{bib_A_hy_2018, AUTHOR = {CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {A hybrid deep learning approach for medical relation extraction}, BOOKTITLE = {KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOPS}. YEAR = {2018}}
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.
A hybrid deep learning approach for medical relation extraction
CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
Technical Report, arXiv, 2018
@inproceedings{bib_A_hy_2018, AUTHOR = {CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {A hybrid deep learning approach for medical relation extraction}, BOOKTITLE = {Technical Report}. YEAR = {2018}}
Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.
Medusa: Towards Simulating a Multi-Agent Hide-and-Seek Game
AKSHAT TANDON,Kamalakar Karlapalem
International Joint Conference on Artificial Intelligence, IJCAI, 2018
@inproceedings{bib_Medu_2018, AUTHOR = {AKSHAT TANDON, Kamalakar Karlapalem}, TITLE = {Medusa: Towards Simulating a Multi-Agent Hide-and-Seek Game}, BOOKTITLE = {International Joint Conference on Artificial Intelligence}. YEAR = {2018}}
In the game of hide and seek, one or more agents (hiders) can hide behind objects to prevent other agents (seekers) from seeing them through their line of sight (visibility). These hider agents can be eventually found by seekers who explore the environment. The hide and seek model serves as an abstraction of many real world scenarios such as police agents trying to capture intruders hiding in a warehouse, security/patrolling agents on the lookout for any suspicious activity, spying drones trying to prevent themselves from being caught. Medusa allows modeling such scenarios by supporting the simulation, visualization and analysis of hider and seeker agent strategies under different environments.
Empirical Evaluation of Idle-Time Analysis Driven Improved Decision Making by Always-On Agents
GARAPATI SRAVYASRI,Kamalakar Karlapalem
Annual Computer Software and Applications Conference, COMPSAC, 2018
@inproceedings{bib_Empi_2018, AUTHOR = {GARAPATI SRAVYASRI, Kamalakar Karlapalem}, TITLE = {Empirical Evaluation of Idle-Time Analysis Driven Improved Decision Making by Always-On Agents}, BOOKTITLE = {Annual Computer Software and Applications Conference}. YEAR = {2018}}
Always-on agents are those agents which are like daemon programs that are always on and can do tasks as and when they arrive. These agents are idle when there is no task assigned to them. Further, those agents that work on a task, also wait for some event or task completion, and hence, are also idle for a short duration between execution of the tasks. The question arises what should the agent be doing when it is idle. In this paper, we conduct an empirical analysis to show improved decision-making capabilities by agents when they exploit their idle-time to analyze their past tasks in terms of decisions taken and their impact. The improvement of performance can be measured by considering an increase in success rate, avoiding strategies that may not work, quicker decision making, etc. Our always-on agent, while executing a task, stores some of the pertinent details of the task done in a database, such as decisions taken, paths of execution of the task, goodness of them, etc. The agent uses different strategies to use this stored knowledge. We present and evaluate three strategies that always-on agents can use (i) Frequent Decision Strategy (FDS) - the agent stores the prior executions and their frequency of success and failure, repeats the most frequent successful decision taken during prior executions of the task, (ii) Analyzed Decision Strategy (ADS) - the agent analyzes prior executions that were successful or not, stores in database the goodness of various alternatives and chooses the best alternative and (iii) Online analysis decision strategy (OADS) - the alwayson agent while executing its task, during its idle time analyzes the possible future situations and prepares the list of best possible decisions that can be taken in future. Note that the FDS and ADS are used when the agent is not doing any task and is off-line, whereas, OADS generates new mock tasks to consider possible alternative task execution situations to expand its decision-making scenarios while having a task at hand. We conduct our empirical study on always-on agents playing connect-4 games to check the viability and show improved decision making.
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
RAHUL T,Sujit Rajappa,Michael J. Black,Kamalakar Karlapalem,Aamir Ahmad
International Conference on Safety, Security, and Rescue Robotics, SSRR, 2018
@inproceedings{bib_Dece_2018, AUTHOR = {RAHUL T, Sujit Rajappa, Michael J. Black, Kamalakar Karlapalem, Aamir Ahmad}, TITLE = {Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios}, BOOKTITLE = {International Conference on Safety, Security, and Rescue Robotics}. YEAR = {2018}}
In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.
High Dimensional Clustering: A Strongly Connected Component Clustering Solution (SCCC)
MIHIR SHEKHAR,Lini Teresa Thomas,Kamalakar Karlapalem
International Conference on Data Mining Workshops, ICDM-W, 2018
@inproceedings{bib_High_2018, AUTHOR = {MIHIR SHEKHAR, Lini Teresa Thomas, Kamalakar Karlapalem}, TITLE = {High Dimensional Clustering: A Strongly Connected Component Clustering Solution (SCCC)}, BOOKTITLE = {International Conference on Data Mining Workshops}. YEAR = {2018}}
High dimensional data is often challenging to cluster due to the curse of dimensionality leading to challenges in identifying clusters. The key challenge in high dimensional clustering is to develop a solution that identifies clusters which are as complete as they can be, while not merging well-separated clusters. We propose core points which represent local compact regions. The strongly connected component from the k-nearest neighbor graph of core points provides for a group of points that are strongly mutually connected. These mutually connected regions represent the core structure of the clusters. Our empirical analysis and experimental results present the rationale behind our solution and validate the goodness of the clusters against the state of the art high dimensional clustering algorithms.The novelty of our solution is to use the concept of reverse nearest neighbors to generate natural clusters in high dimensions.
Notability Determination for Wikipedia
P YASHASWI,Kamalakar Karlapalem
International Conference on World wide web, WWW, 2017
@inproceedings{bib_Nota_2017, AUTHOR = {P YASHASWI, Kamalakar Karlapalem}, TITLE = {Notability Determination for Wikipedia}, BOOKTITLE = {International Conference on World wide web}. YEAR = {2017}}
Being the ever-growing online encyclopedia, Wikipedia requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these named entities to meet" notability" standards for their article inclusion. In this paper, we evaluate named entities for their notability by using reliability and entity salience features. Evaluations of our system provide evidence for the viability of our solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.
CCCG: Clique Conversion Ratio Driven Clustering of Graphs
PRATHYUSH KUMAR KASYAP S,Kamalakar Karlapalem
Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2017
@inproceedings{bib_CCCG_2017, AUTHOR = {PRATHYUSH KUMAR KASYAP S, Kamalakar Karlapalem}, TITLE = {CCCG: Clique Conversion Ratio Driven Clustering of Graphs}, BOOKTITLE = {Pacific-Asia Conference on Knowledge Discovery and Data Mining}. YEAR = {2017}}
Networks have become ubiquitous in many real world appli-cations and to cluster similar networks is an important problem. There are various properties of graphs such as clustering coefficient (CC), den-sity, arboricity, etc. We introduce a measure, Clique Conversion Coeffi-cient (CCC), which captures the clique forming tendency of nodes in anundirected graph. CCC could either be used as a weighted average ofthe values in a vector or as the vector itself. Our experiments show thatCCC provides additional information about a graph in comparison torelated measures like CC and density. We cluster the real world graphsusing a combination of the features CCC, CC, and density and show thatwithout CCC as one of the features, graphs with similar clique formingtendencies are not clustered together. The clustering with the use ofCCC would have applications in the areas of Social Network Analysis,Protein-Protein Interaction Analysis, etc., where cliques have an impor-tant role. We perform the clustering of ego networks of the YOUTUBEnetwork using values in CCC vector as features. The quality of the clus-tering is analyzed by contrasting the frequent subgraphs in each cluster.The results highlight the utility of CCC in clustering subgraphs of alarge graph
VAST Mini-Challenge 1
Ayushi Gupta,CHIKKA VEERA RAGHAVENDRA,Kamalakar Karlapalem
IEEE Conference on Visual Analytics Science and Technology, VAST, 2017
@inproceedings{bib_VAST_2017, AUTHOR = {Ayushi Gupta, CHIKKA VEERA RAGHAVENDRA, Kamalakar Karlapalem}, TITLE = {VAST Mini-Challenge 1}, BOOKTITLE = {IEEE Conference on Visual Analytics Science and Technology}. YEAR = {2017}}
We propose an interactive visual analytics system for exploring spatio-temporal data in VAST 2017 Mini-Challenge-1. As part of this challenge, we are expected to determine repeating, seasonal and unusual cars' movements in Lekagul park dataset. We use varied visualizations such as heatmap, sequential sunburst and line plots. Further, we have developed a web deployable ad-hoc system for displaying spatio-temporal information on geographical map
Exploiting regions of influence to visualize class boundaries
PALLAV TINNA,Kamalakar Karlapalem
International Symposium on Electronic Imaging 2016 Visualization and Data Analysis, VDA, 2016
@inproceedings{bib_Expl_2016, AUTHOR = {PALLAV TINNA, Kamalakar Karlapalem}, TITLE = {Exploiting regions of influence to visualize class boundaries}, BOOKTITLE = {International Symposium on Electronic Imaging 2016 Visualization and Data Analysis}. YEAR = {2016}}
Interactive visualization and analysis of the class boundaries is important because it tells us how and why the classes differ. However, the problem of modeling the boundary of classes of arbitrary size, shape and density is challenging. The boundary of a class should not be limited to the points/shape which encloses the points within the class but it should be, the points/shape which encloses the region of influence of a class. The "region of influence" refers to the space around the class where any point lying within the region is likely to be classified to the class based on a nearest neighbor classifier. We have developed interactive boundary visualization toolkit for classified datasets which provides insights about the classifier model used on the dataset. Our algorithm first generates a candidate boundary set for each class based on reverse k-nearest neighbors approach and extends this boundary iteratively through the region of influence of the class. Further, we present these boundary points enclosing the region of influence as a linear approximated shape using triangulation techniques. We show experimental results on 2D and 3D datasets.
Wheeled robots playing chain catch: strategies and evaluation
GARIMA AGRAWAL,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2016
@inproceedings{bib_Whee_2016, AUTHOR = {GARIMA AGRAWAL, Kamalakar Karlapalem}, TITLE = {Wheeled robots playing chain catch: strategies and evaluation}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2016}}
Robots playing games that humans are adept in is a challenge. We studied robotic agents playing Chain Catch game as a Multi-Agent System (MAS). Our game starts with a traditional Catch game similar to Pursuit evasion, and further extends it to form a growing chain of predator agents to chase remaining preys. Hence Chain Catch is a combination of two challenges-pursuit domain and robotic chain formation. These are games that require team of robotic agents to cooperate among themselves and to compete with other group of agents through quick decision making. In this paper, we present a Chain Catch simulator that allows us to incorporate game rules, design strategies and simulate the game play. We developed cost model driven strategies for each of Escapee, Catcher and Chain. Our results show that Sliding slope strategy is the best strategy for Escapees whereas Tagging method is the best method for chain s movement in Chain Catch. We also use production quality robots to implement the game play in a physical environment and analyze game strategies on real robots. Our real robots implementation in different scenarios shows that game strategies work as expected and a complete chain formation takes place successfully in each game.
Semi-Supervised Automatic Generation of Wikipedia Articles for Named Entities.
P YASHASWI,Kamalakar Karlapalem,YARRABELLY NAVYA
International Conference on Web and Social Media Workshops, ICWSM-W, 2016
@inproceedings{bib_Semi_2016, AUTHOR = {P YASHASWI, Kamalakar Karlapalem, YARRABELLY NAVYA}, TITLE = {Semi-Supervised Automatic Generation of Wikipedia Articles for Named Entities.}, BOOKTITLE = {International Conference on Web and Social Media Workshops}. YEAR = {2016}}
We investigate the automatic generation of Wikipedia articles as an alternative to its manual creation. We propose a framework for creating a Wikipedia article for a named entity which not only looks similar to other Wikipedia articles in its category but also aggregates the diverse aspects related to that named entity from the Web. In particular, a semi-supervised method is used for determining the headings and identifying the content for each heading in the Wikipedia article generated. Evaluations show that articles created by our system for categories like actors are more reliable and informative compared to those generated by previous approaches of Wikipedia article automation.
Methods, systems, and computer-readable media for providing a query layer for cloud databases
SATYANARAYANA V R,V. Bharath,Kamalakar Karlapalem,P RADHA KRISHNA
United States Patent, Us patent, 2016
@inproceedings{bib_Meth_2016, AUTHOR = {SATYANARAYANA V R, V. Bharath, Kamalakar Karlapalem, P RADHA KRISHNA}, TITLE = {Methods, systems, and computer-readable media for providing a query layer for cloud databases}, BOOKTITLE = {United States Patent}. YEAR = {2016}}
A method, non-transitory computer readable medium, and apparatus for receiving data from a cloud database. One or more queries requesting data from the cloud database are received. The one or more queries are converted from a row-store database query into a column-store database query. An optimal join plan is identified for the one or more queries using a cost based optimizer based on metadata for one or more relations in the cloud database. The optimal join plan is executed using a cloud application programming interface.
Modeling dynamic relationship types for subsets of entity type instances and across entity types
P RADHA KRISHNA,Anushree Khandekar,Kamalakar Karlapalem
Information systems, ISY, 2016
@inproceedings{bib_Mode_2016, AUTHOR = {P RADHA KRISHNA, Anushree Khandekar, Kamalakar Karlapalem}, TITLE = {Modeling dynamic relationship types for subsets of entity type instances and across entity types}, BOOKTITLE = {Information systems}. YEAR = {2016}}
In a traditional ER model, once we specify a subclass or superclass relationship, any changes to that relationship are treated as schema evolution. Further, ER models are rigid in the sense that once a relationship type is specified across a set of entity types, an instance of relationship type occur when one instance of all participating entity types are specified. Therefore, it is difficult to introduce in a simplified manner all relationship types across subsets of given set of entity types. In this paper, we provide mechanisms to model in our extended ER model: (i) specification of dynamic relationship types across subsets of instances of entity types, (ii) a simplified specification of relationships across subsets of given set of entity types, and (iii) mapping our extended ER model to relational database schema. We also show through an e-contract example the utility of our extended ER model.
Design and Evaluation of Alternate Enumeration Techniques for Subset Sum Problem
AVNI VERMA,Kamalakar Karlapalem
Technical Report, arXiv, 2016
@inproceedings{bib_Desi_2016, AUTHOR = {AVNI VERMA, Kamalakar Karlapalem}, TITLE = {Design and Evaluation of Alternate Enumeration Techniques for Subset Sum Problem}, BOOKTITLE = {Technical Report}. YEAR = {2016}}
The subset sum problem, also referred as SSP, is a NP-Hard computational problem. SSP has its applications in broad domains like cryptography, number theory, operation research and complexity theory. The most famous algorithm for solving SSP is Backtracking Algorithm which has exponential time complexity. Therefore, our goal is to design and develop better alternate enumeration techniques for faster generation of SSP solutions. Given the set of first n natural numbers which is denoted by Xn and a target sum S, we propose various alternate enumeration techniques which find all the subsets of Xn that add up to sum S. In this paper, we present the mathematics behind this exponential problem. We analyze the distribution of power set of Xn and present formulas which show definite patterns and relations among these subsets. We introduce three major distributions for power set of Xn: Sum Distribution, Length-Sum Distribution and Element Distribution. These distributions are prepossessing procedures for various alternate enumeration techniques for solving SSP. We propose novel algorithms: Subset Generation using Sum Distribution, Subset Generation using Length-Sum Distribution, Basic Bucket Algorithm, Maximum and Minimum Frequency Driven Bucket Algorithms and Local Search using Maximal and Minimal Subsets for enumerating SSP.
Crowd Congestion and Stampede Management through Multi Robotic Agents
GARIMA AHUJA,Kamalakar Karlapalem
Technical Report, arXiv, 2015
@inproceedings{bib_Crow_2015, AUTHOR = {GARIMA AHUJA, Kamalakar Karlapalem}, TITLE = {Crowd Congestion and Stampede Management through Multi Robotic Agents}, BOOKTITLE = {Technical Report}. YEAR = {2015}}
Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Then, we use robotic agents to aid the crowd management police in controlling the crowd in these stampede prone areas. While doing so, we aim for minimum interference by robotic agents in our environment. Thereby not disturbing the ambiance and aesthetics of the place. We evaluate the effectiveness of our model in dealing with difficult scenarios like emergency evacuation and presence of localized congestion. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility of robotic agents for detecting and reducing congestion.
Exploiting Near Time Forecasting From Social Network To Decongest Traffic
DEEPIKA PATHANIA,Kamalakar Karlapalem
Technical Report, arXiv, 2015
@inproceedings{bib_Expl_2015, AUTHOR = {DEEPIKA PATHANIA, Kamalakar Karlapalem}, TITLE = {Exploiting Near Time Forecasting From Social Network To Decongest Traffic}, BOOKTITLE = {Technical Report}. YEAR = {2015}}
Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.
Effective Handling of Urgent Jobs-Speed Up Scheduling for Computing Applications
YASH GUPTA,Kamalakar Karlapalem
Technical Report, arXiv, 2015
@inproceedings{bib_Effe_2015, AUTHOR = {YASH GUPTA, Kamalakar Karlapalem}, TITLE = {Effective Handling of Urgent Jobs-Speed Up Scheduling for Computing Applications}, BOOKTITLE = {Technical Report}. YEAR = {2015}}
A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains-Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.
Social network driven traffic decongestion using near time forecasting
DEEPIKA PATHANIA,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2015
@inproceedings{bib_Soci_2015, AUTHOR = {DEEPIKA PATHANIA, Kamalakar Karlapalem}, TITLE = {Social network driven traffic decongestion using near time forecasting}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2015}}
Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens research area to build a framework for traffic management that can capture essential parameters of real-life behavior and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of such framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. To show the utility of the framework, we present experimental studies of few strategies on a public transport system.
Managing multi robotic agents to avoid congestion and stampedes
GARIMA AHUJA,Kamalakar Karlapalem
International Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2015
@inproceedings{bib_Mana_2015, AUTHOR = {GARIMA AHUJA, Kamalakar Karlapalem}, TITLE = {Managing multi robotic agents to avoid congestion and stampedes}, BOOKTITLE = {International Conference on Autonomous Agents and Multiagent Systems}. YEAR = {2015}}
Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Further, we use robotic agents for aiding the crowd management police in controlling the crowd in these stampede prone areas. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility and viability of robotic agents for detecting and reducing congestion.
Identifying medical terms related to specific diseases
MIHIR SHEKHAR,CHIKKA VEERA RAGHAVENDRA,LINI TERESA THOMAS,Sunil Mandhan,Kamalakar Karlapalem
International Conference on Data Mining Workshops, ICDM-W, 2015
@inproceedings{bib_Iden_2015, AUTHOR = {MIHIR SHEKHAR, CHIKKA VEERA RAGHAVENDRA, LINI TERESA THOMAS, Sunil Mandhan, Kamalakar Karlapalem}, TITLE = {Identifying medical terms related to specific diseases}, BOOKTITLE = {International Conference on Data Mining Workshops}. YEAR = {2015}}
We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The results illustrate that our model for disease term classification finds all disease term classes with an average F-score of 0.966.
Structure Simulation of Pentapeptide with Real valued Genetic Algorithm
MADHU SMITA,Harjinder Singh,Abhijit Mitra,Kamalakar Karlapalem
International Conference on Bioinformatics and Drug Discovery, Bioconvene, BDDB, 2008
@inproceedings{bib_Stru_2008, AUTHOR = {MADHU SMITA, Harjinder Singh, Abhijit Mitra, Kamalakar Karlapalem}, TITLE = {Structure Simulation of Pentapeptide with Real valued Genetic Algorithm}, BOOKTITLE = {International Conference on Bioinformatics and Drug Discovery, Bioconvene}. YEAR = {2008}}
The problem of predicting the 3D native conformation of a polypeptide given the primary sequence is of fundamental importance in contemporary biology. Here we present a novel Genetic algorithm method to obtain the optimal geometry of a pentapeptide. The pentapeptide moiety is found in wide set of configurations in protein databases. A genetic algorithm utilizes optimization procedure similar to natural genetic evolution. Unlike other genetic algorithms, which are constrained to operate on bit strings, the algorithm used here operates on real values. A random population of conformations is generated taking into account the constraints of Ramachandran plot. This population advances through successive generations in which the solutions evolve via genetic operators (Mutate, Variate and Crossover); in such a way that it gives both better survival chance to fitter soluctions and a larger diversity within the population. More attention will be given to favorable local structures while unfavorable local structures will be rapidly abandoned on the basis of their fitness functions (potential energy), which are calculated using the GROMOS-96 force field of Spdbv version-3.7. The SCWRL3.0 program developed at Dunbrack lab is used for placing side chains to a fixed backbone coordinates. This algorithm proved to be very efficient for the penta-peptide Met-Enkephalin, a natural Opioid polypeptide produced in the brain. The algorithm converged only in 41 minutes and in 23 generations keeping the population size fixed (20 individual per population). The converged structure is energetically as stable as the native conformation of the protein. Its shows a RMSD of 5.9 from the original native conformation.
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