DEEP LEARNING : DESIGNING INTELLIGENT EXPERTS

Manas GaurDelhi Technological University

OUTLAY OF THE PRESENTATION What is Computational Social Science?

Need for Computational Social Science

ELECTION PREDICTION : HOT FIELD for Computational Social Science

BLENDING-Machine Learning and Computational Social Science

What is S.L.E.P.S

Architecture of SLEPS

Functional Requirement of SLEPS

Class Diagram of SLEPS

Sequence Diagram of SLEPS

Component Diagram of SLEPS

Conclusion and Future Work

References

WHAT IS DEEP LEARNING Deep Learning is a new area of Machine Learning research.

The objective of Deep Learning is moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.

Different Algorithms in Deep Learning :

Restricted Boltzmann Machine

Deep Belief Networks

Logistic Regression in Deep Learning

Neural Network

SUPERVISED LEARNING

Testing:What is ?is?

SEMI SUPERVISED LEARNING

Testing:What is ?is?

SELF TAUGHT LEARNING

Testing:What is ?is?

Samples from Full posteriorinference

Samples from Feed-forward Inference (control)

Input images

Hierarchical Probabilistic Inference

SELF LEARNING ELECTORAL RESULTS PREDICTION SYSTEM (SLEPS)

DELHI TECHNOLOGICAL UNIVERSITY

COMPUTATIONAL SOCIAL SCIENCE

WHAT IS COMPUTATIONAL SOCIAL SCIENCE Computational Social Science (CSS) is the science the focuses on investigating

areas affecting Election Results Prediction :

Social Media and Social Network Analysis

Behavioural Analysis : Psychological Analysis, Geographic Comment Analysis, Sentiment Analysis.

Physiological Trait Analysis and Competence Judgement based on an Event

Response Time Analysis based on Scenario ( Image, Movie, Audio)

Deliberation in judgement based on Gender, Place of Birth, Native Tongue and Genealogy.

Study of Impact of any Event whether Positive or Negative that directly or indirectly influence the Comments of the People and that makes up HEADLINE!!.

WHAT IS CSS (CONTD.) Other Potential Application Areas of Computational Social Science are:

ENVIRONMENT RECOGNITION : Adaptation to New Location, New Language and Vocal Transformation.

Organizational Behaviour : Code of Conduct, Managerial Hierarchy.

Detecting Friendly, Flirtatious, Awkward and Assertive Speech in Speech Dates.

Social Network Research in Higher Education

NEED FOR CSS There is tremendous need for transformation form static social science to

more dynamic and quantitative computational social science due to following reason

High information content on Social web and Trading Web , example : Twitter (#) and Wall Street Journal

Sentiments in the comments : Through Likes, replies, image { emoticons}

Responses to Queries, News and Judgement, Recognition Judgement

Hits on the site, for Example comparing site access and reviews using two case studies of TRIVAGO and IBIBO.com

Revolutionize Organizational Behaviour based on Customers response For example a detailed study at MIT showed that Expedia’s customer service was improved based on the number of complaints by the customers.

ELECTION PREDICTION : HOT APPLICATION OF CSS CSS incorporate foundation topic of vibrant field like Sociology, psychology,

Mathematics, History and Geography with real role played by TIME TRAVEL!!

During the months of elections, there is large amount of data (near about 100 TB) on the table, which if properly analysed can augur the election result.

We try to map data ( both numerical and Boolean ) of any presidential or gubernatorial election using social traits to statistical learning using neural networks, support vector machines.

This create a new field, that combines Social Behaviours with high end statistics, more profoundly called Machine Learning aided Computational Social Science (ML-CSS)

ELECTION PREDICTION: HOT APPLICATION OF CSS Days of Election has seen rise in Twitter Tweets, Facebook status update.

Moreover, people pass comments ( in Favour or Against) based on

Facial Appearances

Standard Trait Based Analysis ( Character Sketch)

Geographical Background

Educational Background

Gender Based Vote Casting

Ethnicity based Vote Casting

BLENDING ML AND CSS ML-CSS Machine Learning : It is a field of intelligence and learning that perfectly

incorporates statistical techniques to educate a system based on clustering and classification.

FOOD for Machine Learning is a data set comprising of independent and dependent attributes.

What CSS Contains?

Web-metrics Data { Social Networking site, Forums, Blogs, Yahoo Portal}

News data { NDTV, AAJ TAK, BBC, CNN-IBN}

Historical Data (Data Warehouse) { Mostly used in election prediction}

Video Clips, Audio Clips and Reviews { by Experts, by third party analyst etc}

POPULAR ML TECHNIQUES IN CSS Neural Networks :

Support Vector Machine:

Fuzzy Logic:

Association Rules:

WHAT IS SLEPS Self Learning Electoral Prediction System (SLEPS) is a system being

proposed to integrate Machine learning and Computational Social Science to increase the credibility and authenticity of electoral polls.

SLEPS is a proposed model that uses large heterogeneous dataset along with dual neural network for efficient electoral result prediction.

SLEPS uses heterogeneous data set stored in various data bases and feed it into a neural network after assigning a proper set of hidden neurons.

The first layer Neural Network is trained and output of which is submitted to 2nd layer neural network.

After the neural network is trained, it is tested on real time data

SLEPS INTERNAL The proposed system is called self learning due to presence of flexible

table structures which expands column wise.

SLEPS model incorporates all features that affect elections prediction process that is

Facial appearance with varied response time (100ms, 250ms, 1s, 2s, Unlimited)

Unreflective and Deliberate judgement by the voters

Character Traits like Trustworthiness, Attractiveness, Integrity etc.

Influence on vote share, competence judgement and electoral outcomes (dependent variables)

Background data of the Contestant

SLEPS DATABASE SCHEMAS Layer 1 Schemas

Character_Traits {Contestant, Trustworthiness, Attractiveness, Likeability, Authenticity, Integrity, Competence_judgement}

All the attributes are scaled from 0-9 in which competence judgment is layer one output ( class label)

Participant_Response_Traits { Contestant, Binary Competent, Continuous Judgement, Recognition Judgement, Competence Judgement}

Binary Competent is scaled from 0-1 { yes or no}

Continuous Judgement is scaled from 0-9. The faces of the contestant are shown in cycle continuously.

Recognition Judgement is scaled from 0-1. This field reflects people’s behaviour when they look for similarity in faces of contestants.

SLEPS DATABASE SCHEMAS The Participant Response Trait is created for different response time wiz {100ms, 250ms, 1s,

2s, unlimited}

SLEPS plot the graph between the competency point and time exposure for different contestant.

SLEPS calculated influence parameter taking all the subsets of the field (2^n).

For example: 100ms exposure for Gubernatorial election, 250ms exposure to improve the prediction by calculating the change in competence judgement

Judgement_Types {Contestant, Unreflective Judgement, Deliberate Judgement, vote share, Competence Judgement }

Deliberate judgement and unreflective judgement is calculated for 3 different categories

Careful thinking and good judgement ( unconstrained time)

Response Deadline within 2s

250ms replication condition

SLEPS DATABASE SCHEMAS Contestant_Traits { Contestant, Work_done, Family_BG, Category,

Ethnicity, Gender, Competence Judgement}

Work_Done : Work done by the contestant in past mapped on scale 0-9

Family_BG : Family Background of the contestant mapped on scale 0-9

Category : To Which category the contestant belong on scale 0-9

Ethnicity : To which religious class the contestant belongs (based on ethnic classes in the country)

Gender Based : Gender of the contestant on scale 0-1

SLEPS DATABASE SCHEMA Layer 2 neural network schemas

Layer2Schema { Contestant, Vote_share, Election_outcomes}

Contestant : it is the primary key in all the database schemas

Vote_Share: it is count of number of votes given by the participant to the contestant

Election_outcomes : it is a binary field which is categorized into {winner, runner-up}

All the Schemas used in SLEPS are flexible but provides prediction based on pervasive influential parameters.

Election_Outcomes is the final output dependent parameter of SLEPS

ARCHITECTURE OF SLEPSback propagation Can be modified based on

following ways1. Added Association Rules2. Can be integrated with

Fuzzy Logic3. Change in the hidden

layer activation function4. Following figure is for

one schemas, similar structure has to be followed for all the SLEPS schemas

5. SLEPS functionality is integrated with MYSQL and Web Help

SLEPS IN ACTION

Dual Neural Network training in SLEPS with convergence in 300 epochs and 400 epochs

SLEPS IN ACTION

Error Histogram with 20 bins. Error = target-output

Performance based comparison between three machine learning techniques (MLT)

Analysis of dataset with regression and root mean square values

SLEPS IN ACTION

0

2

4

6

8

10

0

2

4

6

8

10

12

SLEPS OUTPUT

Expert 1 Expert 2 Expert 3Expert 4 Election Outcome

Expert 1 Expert 2 Expert 3 Expert 4

Election Outcome Vote Share

5 5 5 5 10.5 515975 6 4 6 10 9109105 3 4 5 10.4 1408705 4 4 5 10.3 3182935 4 4 5 10.3 3533155 5 3 5 9.6 1723875 5 2 5 8.7 938645 5 2 5 8.7 1229303 3 8 6 2.2 1.10E+075 5 4 5 10.2 463278

9 8 8 9 1.9 1.98E+07

5 5 6 5 7.336 5516335 2 2 4 9.364 513235 6 4 6 10 2927115 6 4 6 10 3108545 2 2 4 9.36 524435 8 4 7 9.992 1748705 6 6 6 10 2766565 6 6 6 10 3051105 4 5 5 10.1 5781365 7 7 7 6.8 12542905 4 1 5 7.5 419984 7 5 6 6.5 1773256

CLASS DIAGRAM FOR SLEPS

Class diagram showingOne to one Correspondence Among the modules

SEQUENCE DIAGRAM OF SLEPS

The sequence diagram of SLEPS Showing the timely passes of Message in SLEPS in each generation

COMPONENT DIAGRAM OF SLEPS

Component Diagram of SLEPSRepresents all the executable Modules in SLEPS

CONCLUSION AND FUTURE WORK SLEPS is an efficient proposed election prediction framework based on

facial data of the contestant, background data and opinion data.

SLEPS works towards predicting competence judgment.

SLEPS work on dual Neural Network Model which provides election outcomes.

SLEPS can be build further to predict election of any country, presidential and gubernatorial election.

SLEPS is a self learning election prediction system which is continuous evolving with the help of web module incorporated in it.

REFERENCES “A computational approach to politeness with application to social factors”  Cristian Danescu-

Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, Christopher Potts. Proceedings of ACL, 2013. Nominated for Best Paper Award.

“Clash of the Contagions: Cooperation and Competition in Information Diffusion”  by S. Myers, J. Leskovec. IEEE International Conference On Data Mining (ICDM), 2012.

“Classroom Ordering and the Situational Imperatives of Routine and Ritual”  by David Diehl, and Daniel A. McFarland. Sociology of Education 85, 4: 326-349. 2012.

“Defining and evaluating network communities based on ground-truth”  by Jaewon Yang and Jure Leskovec. IEEE International Conference On Data Mining (ICDM). Brussels, 2012.

“Detecting Friendly, Flirtatious, Awkward, and Assertive Speech in Speed-Dates”  by Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland.  Computer Speech and Language 27, 1: 89-115. 2012.

“Differentiating Language Usage Through Topic Models”  by Daniel A. McFarland, Christopher D. Manning, Daniel Ramage, Jason Chuang, Jeffrey Heer, and Dan Jurafsky. In press. Poetics.  118 (6), 1596-1649. 2013.