Dr. Jude W. Shavlik
Professor
Department of Computer Sciences &
Department of Biostatistics & Medical Informatics,
University of Wisconsin-Madison


Biography:

   Jude W. Shavlik is a professor in the Department of Computer Sciences and the Department of Biostatistics & Medical Informatics at UW-Madison. He earned his Ph.D. at the University of Illinois in 1988 and joined the UW faculty that same year. His research interests include learning algorithms that take advice; relational learning; reinforcement learning; transfer learning; machine learning; and data mining applied to biomedical tasks such as disease diagnosis, protein-structure determination and information extraction from online biomedical text.

Topic :

Thirty Years of Combining Symbolic and Numeric Learning


Abstract:  

For nearly 30 years, Dr Jude's research group has investigated the use of domain knowledge, expressed in some version of mathematical logic, that is refined or exploited by numeric-based learning algorithms. These include what we called knowledge-based neural networks and knowledge-based support vector machines. Key ideas of these methods, as well as the behind-the-scenes motivations that lead to them will be covered in his session and also describes why we switched from using the phrase 'prior knowledge' to using 'advice.' Finally, some of their recent work on Markov Logic Networks, which can be viewed as a knowledge-based graphical model will be discussed.





Dr. Fabia U. Battistuzzi
Assistant Professor
Department of Biological Sciences
Oakland University

Biography:

   Dr. Fabia U. Battistuzzi is an Assistant Professor in Evolution at Oakland University. She obtained her PhD from the Pennsylvania State University in 2007 in Evolutionary Biology and Astrobiology with a special focus on bioinformatics. She has further specialized in evolutionary medicine during her postdoctoral training. Her research interests include evolution of early life, evolution of diseases, phylogenomics, and molecular evolution.

Topic :

The role of Big Data in biology and medicine


Abstract:  

The historical constraint on data acquisition that was characteristic of most biological fields has been lifted by the advent of next generation sequencing technologies. The past decade has seen a dramatic increase in data availability and types, from whole genome sequences to complex interactive maps of gene functions and products, which have a high potential of revealing the inner workings of biological organisms during their evolution, development, healthy, and diseased conditions. However, this vast amount of information is often difficult to analyze and visualize and challenges are arising to identify patterns within the data. A transdisciplinary approach to study biology is therefore key to improve our power and accuracy to interpret data and reveal new dimensions to biological information. To achieve this, it is fundamental to apply knowledge from computer science, graph theories, and data modelling to the biological realm in order to interpret, test, and represent complex patterns in effective and accurate ways. The growing fields of bioinformatics and evolutionary medicine are two examples of the applications of Big Data science to biology to obtain patterns in organismal evolution, development, genome function, and disease epidemiology. Discoveries within these fields are guiding major innovations in space science, conservation biology, and medicine and are advancing our ability to use and interpret genomes as data repositories of past events and predictors of future responses.





Dr. Mak Sharma
Head of School of Computing and Digital Technology
Birmingham City University



Biography:

   Head of School Mak Sharma is internationally recognised for his expertise in the use of vendor resources such as Cisco, Microsoft and Oracle, and embedding these into various qualifications. Mak joined the University over 20 years ago and during this period, he has worked on a number of projects the largest and most prestigious of which was the Millennium Point Project. Mak’s research interests are in the employability of graduates and his current technical interests are in the field of Network Management, and Health Informatics and Cloud.

Topic :

IoT, Data and decision support


Abstract:  

With more devices than the number humans on our planet (50B+ devices) being connected to Internet by 2020 and our every move captured by sensors of some kind, whether that is in retail, healthcare or transportation, the captured data is of value to someone. With the exponential increase in the use IoT devices, we need to harness the collective power of the devices, the data and the resulting revenues that are potentially huge. As with all new revolutions, there are positive benefits as well some negatives, and we need engineers to manage the technical changes and business leaders to create new business models for a better world.

In order to have a global impact, we need to start with multi disciplinary education, showing the future entrepreneurs and engineers the value of IoT. We also need to research the current use and future use of technologies to enhance society and the world. The use of such disruptive technologies will give rise to undesirable outcomes and we need to mitigate against these potential issues, such as the IP Camera hack in the US recently that caused problems for some famous service providers.

So how are we helping the customer? there are well know examples such the Jonnie Walker Blue bottle which has intelligence to detect it has been opened and hence provide a personalised experience based on the use of a smart phone and the dedicated message on each bottle. Other examples include the intelligent tabs on Virgin cargo, or Rolls Royce adding more sensors to measure, monitor, maintain, the engine parameters remotely for an optimised flight. In this last example machine are communicating with machines, with little intervention from humans, this is because we have set up the parameter limits to trigger other machine events and based on AI we have implemented; the machines will make these decisions much faster, more acute and more repeatable the we can. All this data and decision making can then used to create more and better dedicated experiences.

In this keynote, the main discussion points are: how IoT is disrupting our current way of life generally for the better; What IoT means from an academic perspective, method of teaching using practical techniques based rather than theory and how to encourage graduates to embrace IoT. Then summarisation of Decision Support System, their uses and applications, and how these are used today and how we could create more value from the data. Finally explanation about 2 research projects that fit with the IoT domain and further examples of IoT applications that can exploited globally.





Dr. Amit Kumar Saxena
Professor
Dept of Computer Science & Information Technology
Guru Ghasidas University

Biography:

   Prof Amit Kumar Saxena, was awarded Ph D in 1998 in Computer Science mainly on Artificial Neural Networks. He has several publications in National, International journals, IEEE transaction and proceedings of conferences. He has visited various countries for academic assignments such as Malaysia, Singapore, Kuwait, USA, Spain, Taiwan. Prof Saxena has delivered invited and keynote addresses at International Conferences in India and abroad. He has authored a book on C Programming; he is also reviewer and editor of journals and conferences. Presently Prof Saxena is a Professor and Head, Dept of Computer Science and Information Technology, G G Central University Bilaspur CG State of India. His research specialization includes Soft Computing, Computational Intelligence, Dimensionality reduction for feature selection, evolutionary computing.

Topic :

Big Data, Cloud Computing and Internet of Things (IoT): Concepts with Issues


Abstract:  

With the advent of ARPAnet, the viability of internet was brought to common people. By and by the use of internet was extended to every area of society to transmit and receive data. The internet served as the backbone to connect various computing devices mainly desktop or laptop computers. With the tremendous development in communication devices, now internet has become an indispensible icon to connect smart phones, tablets, i-phones etc. Kevin Ashton in 1999 introduced a new term Internet of Things, abbreviated as IoT in the context of Supply Chain Management. IoT is vigorously expanded to its current form and mostly defined as an internet of all physical objects, animate or inanimate. The connected objects can speak to each other via sensors, RFIDs, without human intervention. Humans can control the physical objects, even the humans (particularly patients, kids, physically challenged) for various purposes. The applications of IoT have a variety of range.The individual homes; offices and electronic gadgets can be monitored and controlled by IoT devices. Personal health care can also be watched thru these devises. The elderly or small kids at homes in particular can be monitored by sensors, actuators by doctors or even by their family members sitting in their respective offices without need of engaging nurses to look after them. The smart environment is another vital application of IoT.

Another significant development in the area of data based technology has been ‘big data’. In fact big data and IoT are regarded as two faces of the same coin. The big data refers to a situation where normal or conventional data management techniques such as SQL cannot manage such data due to its huge size. It is well known how dependency on software applications is increasing day to day by virtue of bio matrix devices, security devices, biopsy of human beings, radar and other military based devices, share markets, leisurely arrangements of people, travel reservation data, ERP etc. All such applications will lead to heaping mammoth amount of data. It is assumed that about 50 billion devices will be connected with internet through IoT, all these devices will also accumulate huge amount of data. There will naturally be a threat of leaking private data of individuals and organizations which will depend on these devices. The coordination among these devices will need high attention. Moreover, how many connecting devices one will wear or take care of.

With the tremendous software applications ready to enter in society, several support systems will be required. Obviously industries will hire cloud services for their requirements. Clouds not only provide secured storage but also services and platform for smooth functioning by its users. A compromised server could significantly harm the users as well as cloud providers. Unfortunately users may not be aware what type of their information could be stolen, including credit card and social security numbers, addresses, and personal messages.

Although technology on one end offers attractive and optimistic approaches to deal with our routines in life yet on the other end also alarms us to be aware of its limitations to a certain extent. Data however will be central in the operations of all such technological developments. Other issues to be addressed while adopting big data, IoT and cloud computing will be privacy, security, architecture, design and implementation, networking, connectivity along with existing infrastructure.





Dr. S.D Madhu Kumar
Associate Professor
Department of Computer Science and Engineering
NIT Calicut



Biography: 

Dr. S.D Madhu Kumar is an Associate Professor in the Department of Computer Science and Engineering at NIT Calicut. He obtained his PhD in Computer Science from IIT Bombay. His research specialization includes Distributed Computing, Cloud Computing, Big Data & Cloud Database and software Engineering.


Topic :

Big Data Modelling and Querying with Graph Databases


Abstract: 

Graphs are extremely useful in understanding a large variety of datasets and are widely used in the database world. Graph Databases use Graph structures like nodes, edges and properties to represent and store data of the real world. Edges represent relationships which directly relate data items in the data store. The relationships allow data in the store to be linked together directly and retrieved with a single operation unlike in the relational model , where links between data are stored in the data itself, and are obtained by searching for this data within the store and using operators like the Join. Graph databases are designed to allow simple and rapid retrieval of complex hierarchical structures that are difficult to model in relational systems. Graph databases offer an extremely flexible data model. When compared to relational databases, where join-intensive query performance deteriorates as the size of the dataset increases, with a graph database performance tends to remain relatively constant, even as the size of the dataset grows bigger. This is because queries are localized to a portion of the graph. As a result, the execution time for each query is proportional only to the size of the part of the graph traversed to satisfy that query, rather than the size of the whole graph. Relationships in a graph form paths. Querying—or traversing—the graph involves traversing through paths. Because of the fundamentally path-oriented nature of the data model, the majority of path-based graph database operations are highly aligned with the way in which the data is laid out, making them extremely efficient. With the growing importance of graph databases, there exists a variety of query languages for graph databases. GRAM, GraphDB, G, Cypher Query Language are few examples for Query languages used in this context.




Dr. S.D Madhu Kumar
Associate Professor
Department of Computer Science and Engineering
NIT Calicut



Biography: 

Dr. S.D Madhu Kumar is an Associate Professor in the Department of Computer Science and Engineering at NIT Calicut. He obtained his PhD in Computer Science from IIT Bombay. His research specialization includes Distributed Computing, Cloud Computing, Big Data & Cloud Database and software Engineering.


Topic :

Cloud Storage- Advances, Challenges and Opportunities


Abstract: 

The primary use of cloud storage today is for storage of unstructured data, which is the fastest growing and most voluminous content, causing many administrative problems. Cloud storage option enables the users and organizations to store their data remotely and enjoy good quality applications on demand without having any burden associated with local hardware resources and software managements. But there are many issues associated with storing data in the cloud. Security of the data, Ensuring the correctness of the data, Fault tolerance, meeting the consistency requirements of the applications in a multi user environment, Performance, reliability and scalability are some of the challenging issues.

There are many techniques existing today, which in turn provide correctness of data in cloud, like Merkle Hash Tree (MHT), Distributed erasure-coded data and flexible distributed storage integrity auditing mechanism. Erasure coded storage scheme offers a promising future for cloud storage. Highlights of erasure coded storage systems are that these offers same level of fault tolerance as of replication at a lower storage footprint.

This tutorial will give a glimpse into the advances, technical challenges and research issues in the cloud storage sector. There will a special focus on erasure coded storage systems and simulation tools that can be used for testing the erasure coded storage systems for cloud.





Mr. Rahul Agrawal
Principal Machine Learning Manager
Microsoft


Biography: 

Rahul Agrawal leads a team of engineers and scientists in Microsoft Bing Ads. His team is responsible for query intent understanding and matching it to advertiser's intent. His primary interest is in large scale machine learning algorithms, language understanding and deep learning architectures. Prior to Microsoft he was with Yahoo labs where he was responsible for click prediction for Yahoo display advertising.


Topic :

Deep Learning and Applications to Language Understanding


Abstract: 

In this tutorial, we will start with the basics of deep learning and study how it can be applied to various tasks in language understanding. We will look at how Deep Learning compares with respect to conventional bag-of-words representation. We will also look at how deep learning algorithms stack up when solving problems such as NER, segmentation, classification and document clustering. We will also look at various deep learning based architectures and how embedding play a role in intent representation.





Dr. V. Vaidehi
Dean, SCSE
VIT university, Chennai


Biography: 

Dr. V. Vaidehi received her BE (ECE) from College of Engineering, Guindy, ME (Applied Electronics) and Ph.D. in the area of Parallel Processing from Madras Institute of Technology, Anna University, Chromepet, Chennai. She was task team member in Micro Satellite (ANUSAT) and Executed several funded project in the area of Target Tracking, Multi-Sensor Fusion, Semantic Intrusion Detection System and Complex Event Processing. She has served as the Head of Computer Centre, Head of Electronics department, Head of Computer technology, Head of Information Technology and Director of AU-KBC Research Centre, MIT, Anna University, Chennai and Chairman of Faculty of Information and Communication Engineering, Anna University, Chennai. Currently she is Dean, School of Computer Science and Engineering, VIT University, Chennai. Her research interest includes Networking, Parallel and Distributed Processing, Adaptive Digital Signal Processing, Image and Video Processing, Network and System Security.

Topic :

Automated Pentesting for faster Security Assessment


Abstract: 

Automating penetration testing can be used to test the insecurity of an application. It is conducted to find the security risk which might be present in the system. If a system is not secured, then any attacker can disrupt or take authorized access to that system. Security risk is normally an accidental error that occurs while developing and implementing the software. Penetration testing is essential because it can identify the simulation environment i.e., how an intruder may attack the system. It helps in finding the weak point of the system where attacker make use of the weak point to exploit the target.

Alerts

The following paper ID's have been notified to submit the extended version of their work to IJBDI   
Paper ID's : 2, 9, 21, 37, 47, 57, 61, 62, 79, 81
Conference Proceeding will be published in Springer CCIS Series (Final approval pending)
Selected papers will be considered for publication in special issue Journal . See Call For Papers

Important Dates

Event Date
Paper Submission deadline 24th August, 2016
Final Camera-ready papers due 25th October, 2016
Early Registration 25th October, 2016
Late Registration 31st October, 2016
Tutorial Participation Registration 15th November, 2016
30th December 2016
Pre-Conference Tutorial 6th December, 2016
3rd January 2017

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