Dr. Stephanie Moore is Assistant Professor in the Organization, Information, and Learning Sciences program at the University of New Mexico and is the Editor-in-Chief of the Journal of Computing in Higher Education. She is also a speaker for the United States Speakers Program at the US Department of State through which she consults with foreign embassies on effective online learning and planning, and she is a Fellow with the Barbara Bush Foundation. Her work with the foundations focuses on the use of learning technologies for adult literacy and guidance for foundations wishing to invest in the development in learning technologies. Her research also includes such projects as the use of performance improvement for assessing the needs of new teachers and developing targeted micro-instruction as well as the development of effective online and blended human resources workforce development for the federal government that can lead to eventual certification. Her areas of expertise include online and blended learning, educational / learning technologies, multimedia learning, performance improvement, ethics of technology, and integration of societal impact into the design and planning process. One of her more recent publications was the co-authored article, “The Difference Between Emergency Remote Teaching and Online Learning,” which has been cited over 1,000 times and had a significant impact on conversations in higher education about the impact of COVID and planning in a post-pandemic environment. 

Prior to joining the OILS program, she was Assistant Professor of Instructional Design & Technology in the Curry School of Education and Human Development at University of Virginia where she taught instructional design, performance improvement, online learning, and ethics for learning and workplace technology. In 2018, she received the Casteen Teaching Fellowship, named for the former president of the University of Virginia, and accompanying grant from the Institute for Practical Ethics and Public Life to further develop her work on ethics of learning technologies. Across her career, she has helped to build and lead effective online learning programs that have won multiple awards and recognitions such as the AACTE Innovation of the Year Award and the Leadership in Education award presented by the Southern Piedmont Technology Council. The latest online program for which she led the development is ranked #3 in its category in the US. 

Title of Talk: VAL based DISCOVERY of CFDs

P.V. Kumar

Emeritus Professor, Computer Science & Engg, Osmania University, Hyderabad, India

Professor, Dept. of IT, Anurag University, Hyderabad.

Abstract : Finding Conditional Functional Dependencies (CFD) is emerging area in databases.  Conditional Functional Dependencies are Functional Dependence that hold on fragment of original relation. For a given relation, initially the set of functional dependencies should be derived and then the set of conditional functional dependencies can be obtained.  However finding quality CFDs is an expensive process that involves intensive manual effort. The discovery problem of CFDs is highly nontrivial. To do this, the concept of VAL theory is used, for correct and exhaustive results.   In this context, both constant CFDs and variable CFDs can be found. CFDs are used for maintaining consistency of data by incorporating bindings of semantically related values. In fact, the CFD violations are helpful in finding inconsistencies of data. Especially, the constant conditional functional dependencies can be used for Data Cleaning and data integration purposes. The value of the data depends on its quality that is more difficult to be defined.

Bio of Prof. Dr. P. V. Kumar

P V Kumar is an Emeritus Professor of Computer Science and Engineering at University College of Engineering, Osmania University, Hyderabad. He is awarded Ph.D in Computer Science and Engineering in the area of temporal databases from Osmania University, Hyderabad. Currently, Prof. P V Kumar is working as a distinguished Professor in the Department of Information Technology, Anurag University, Hyderabad. He has more than 35 years of Academic teaching and R&D experience. A number of research scholars are working under his esteemed guidance towards their Ph.D. He has to his credit several quality research papers published in various peer reviewed national and International Journals and has also presented several research papers at National and International conferences. He served as a Chairman, Board of studies, Computer Science and Engineering at Osmania University College of Engineering and has organized and conducted various staff development programs and workshops. He is a Life Member of ISTE. His interested areas include Temporal databases and Temporal data mining, Bio Informatics, Data mining and Artificial Intelligence. Several research scholars from various reputed universities are pursuing research in his guidance and are also awarded successfully.

KEYNOTE SPEAKER-2: Srinivasa Rao Dammavalam

Talk Title: Fusion of Machine Learning, Deep learning, Artificial Intelligence

 for Data Science Applications

ABSTRACT

Looking a decade back, we can figure out that most of the “Data Science” being done in industrial-setup was actually based on arithmetic and elementary statistics like mean, median, variance. In fact, doing a weighted-average or linear-regression was considered as advanced-modelling back then. Now the requirements have increased, and if you want to really understand current ML algorithms, you can’t do without knowing the basics of calculus, linear algebra as well as probability theory. Data Science is now Uncovering the Reality. Data Science is transforming some of the world's biggest companies. At an abstract level, common logistics challenges across industries are i) Efficiency and reducing wait time and ii) Improve reliability. The simplest way to achieve the two objectives is to have infinite supply in the system. In such a case, whenever a request for the said product is made, you will definitely obtain one. However, such a setting is practically infeasible. One of the tools which can be used to achieve the two objectives with minimal costs is Artificial Intelligence. In this talk, the objective is to throw light on Fusion of Machine Learning, Deep learning, Artificial Intelligence techniques for building successful data science applications

BIO OF SPEAKER 

Dr. Srinivasa Rao Dammavalam, is currently an Associate Professor in the Department of Information Technology of VNRVIET, Hyderabad. He obtained Bachelor of Technology in Computer Science & Engg from Acharya Nagarjuna University in 2000 and Master of Technology in Information Technology from JNTU University, Hyderabad in 2007.  He was also a Research Scholar in AVIRES laboratory at University of Udine, ITALY from January 2007 to December 2008 as part of MOU between JNTU HYDERABAD and University of Udine, Italy. Srinivasa Rao Dammavalam is awarded Ph. D degree in the Computer Science & Engineering from JNTUK Kakinada for his thesis on “QUALITY ASSESSMENT OF ITERATIVE IMAGE FUSION BASED ON NEURO FUZZY AND UNSUPERVISED IMAGE CLASSIFICATION” specialized in Image Processing. His extended post Ph.D activity includes advanced certification in “Artificial Intelligence and Machine Learning" from International Institute of Information Technology, Hyderabad.

 He has 17 years of academic teaching experience and several research papers published in various journals and conferences indexed in Scopus and Web of Science. He is a Guest Editor for the several issues in Recent Advances in Computer Science and Communications and has been an active Technical Programme Committee member for several international conferences. He was a session chair for the special track “Contemporary Issues and Challenges in Real Time Image Processing Applications” at ICAICR 2020.  He is also a reviewer for Journal of Computer Science, European Journal of Remote Sensing, International Journal of Image and Data Fusion, Multimedia Tools and Applications, IETE Journal of Research, IJSCAI and Inderscience publishers and few Conferences indexed in Springer conferences CVIP 2017, CVIP 2018, ICCIS 202, ICCCT2021, ICDSA 2021.

 He delivered several guest lectures, expert and invited talks in FDPs, STTPs in various Institutions. His basic research interests are Artificial Intelligence, Computer Vision, Machine Learning, Deep Learning, Data Science, Shadow Removal, Motion Tracking, Image classification, Video Surveillance, Traffic Monitoring, Image fusion, Image analysis.  He guided 16 undergraduate projects and 5 post graduate projects. He has also successfully completed the research project titled “Image fusion using fuzzy and neuro fuzzy logic” from AICTE, RPS during 2011-13 and also received STTP grant from AICTE in 2020. Currently, he is working on the project funded by JNTUH-Hyderabad, TEQIP-III under Collaborative Research Scheme and developed the MobileApp for plant disease classification from this project. He is a Life member in ISTE, IAENG, CSI and professional member of ACM, IEEE.