Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models

Multi-Modalities

Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.

• Equivalence: information is presented in multiple ways and can be interpreted as the same information
• Specialization: when a specific kind of information is always processed through the same modality
• Redundancy: multiple modalities process the same information
• Complimentarity: multiple modalities take separate information and merge it
• Transfer: a modality produces information that another modality consumes
• Concurrency: multiple modalities take in separate information that is not merged

Computer - Human Modalities

Computers utilize a wide range of technologies to communicate and send information to humans:

• Vision - computer graphics typically through a screen
• Audition - various audio outputs

Project Features

Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

Project Demos

Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA

Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk

Assignment (Hands-on Challenge for Attendees)

Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.

• Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
• Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
• With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
• Knowledge Graph infoboxes were added to Google's search engine in May 2012

What is the knowledge graph?

▶Knowledge in graph form!

▶Captures entities, attributes, and relationships

▶More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

▶Structured Search & Exploration
e.g. Google Knowledge Graph, Amazon Product Graph

▶Graph Mining & Network Analysis
e.g. Facebook Entity Graph

▶Big Data Integration
e.g. IBM Watson

▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati

Telematics data have a potential to unlock revenue of 1.5 trillion. Unfortunately this data has not been tapped by many users.

In this case study Karthik Thirumalai would discuss how we can use telematics data to identify driver behaviour and do preventive maintenance in automobile.

The following section describes Telco Domain Real-time advertising based on browsing use case in terms of :

• Potential business benefits to earn.
• Functional use case architecture depicted.
• Data sources (attributes required).
• Analytic to be performed,
• Output to be provided and target systems to be integrated with.

This use case is part of the monetization category. The goal of the use case is to provide a kind of DataMart to either Telecom business parties or external third parties sufficient, relevant and customized information to produce real-time advertising to Telecom end users. The customer targets are all Telecom network end-users.

The customization information to be delivered to advertise are based on several dimensions:

• Customer characteristics: demographic, telco profile.
• Customer usage: Telco products or any other interests.
• Customer time/space identification: location, zoning areas, usage time windows.

Use case requirements are detailed in the description below as “ Targeting method”

1. Search Engine Targeting:

The telco will use users web history to track what users are looking at and to gather information about them. When a user goes onto a website, their web browsing history will show information of the user, what he or she searched, where they are from, found by the ip address, and then build a profile around them, allowing Telco to easily target ads to the user more specifically.

3. Content and Contextual Targeting:

This is when advertisers can put ads in a specific place, based on the relative content present. This targeting method can be used across different mediums, for example in an article online, about purchasing homes would have an advert associated with this context, like an insurance ad. This is achieved through an ad matching system which analyses the contents on a page or finds keywords and presents a relevant advert, sometimes through pop-ups.

4. Technical Targeting

This form of targeting is associated with the user’s own software or hardware status. The advertisement is altered depending on the user’s available network bandwidth, for example if a user is on their mobile phone that has a limited connection, the ad delivery system will display a version of the ad that is smaller for a faster data transfer rate.

5. Time Targeting:

This type of targeting is centered around time and focuses on the idea of fitting in around people’s everyday lifestyles. For example, scheduling specific ads at a timeframe from 5-7pm, when the

6. Sociodemographic Targeting:

This form of targeting focuses on the characteristics of consumers, including their age, gender, and nationality. The idea is to target users specifically, using this data about them collected, for example, targeting a male in the age bracket of 18-24. The telco will use this form of targeting by showing advertisements relevant to the user’s individual demographic profile. this can show up in forms of banner ads, or commercial videos.

7. Geographical and Location-Based Targeting:

This type of advertising involves targeting different users based on their geographic location. IP addresses can signal the location of a user and can usually transfer the location through different cells.

8. Behavioral Targeting:

This form of targeted advertising is centered around the activity/actions of users and is more easily achieved on web pages. Information from browsing websites can be collected, which finds patterns in users search history.

9. Retargeting:

Is where advertising uses behavioral targeting to produce ads that follow you after you have looked or purchased are a particular item. Retargeting is where advertisers use this information to ‘follow you’ and try and grab your attention so you do not forget.

10. Opinions, attitudes, interests, and hobbies:

Psychographic segmentation also includes opinions on gender and politics, sporting and recreational activities, views on the environment and arts and cultural issues.

Introduction

“Alexa, launch Netflix!”

No longer limited to providing basic phone and Internet service, the telecom industry is at the epicentre of technological growth, led by its mobile and broadband services in the Internet of Things (IoT) era.This growth is expected to continue,The driver for this growth? Artificial intelligence (AI).

Artificial Intelligent applications are revolutionizing the way telecoms operate, optimize and provide service to their customers

Today’s communications service providers (CSPs) face increasing customer demands for higher quality services and better customer experiences (CX). Telecoms are addressing these opportunities by leveraging the vast amounts of data collected over the years from their massive customer base. This data is culled from devices, networks, mobile applications, geolocations, detailed customer profiles, services usage and billing data.

Telecoms are harnessing the power of AI to process and analyse these huge volumes of Big Data in order to extract actionable insights to provide better customer experiences, improve operations, and increase revenue through new products and services.

With Gartner forecasting that 20.4 billion connected devices will be in use worldwide by 2020, more and more CSPs are jumping on the bandwagon, recognizing the value of artificial intelligence applications in the telecommunications industry.

Forward-thinking CSPs have focused their efforts on four main areas where AI has already made significant inroads in delivering tangible business results: Network optimization, preventive maintenance, Virtual Assistants, and robotic process automation (RPA)

Network optimisation

AI is essential for helping CSPs build self-optimizing networks (SONs), where operators have the ability to automatically optimize network quality based on traffic information by region and time zone. Artificial intelligence applications in the telecommunications industry use advanced algorithms to look for patterns within the data, enabling telecoms to both detect and predict network anomalies, and allowing operators to proactively fix problems before customers are negatively impacted.

Some popular AI solutions for telecoms are ZeroStack’s ZBrain Cloud Management, which analyses private cloud telemetry storage and use for improved capacity planning, upgrades and general management; Aria Networks, an AI-based network optimization solution that counts a growing number of Tier-1 telecom companies as customers, and Sedona Systems’ NetFusion, which optimizes the routing of traffic and speed delivery of 5G-enabled services like AR/VR. Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning, and to predict service degradations on cell sites up to seven days in advance.

Predictive maintenance

AI-driven predictive analytics are helping telecoms provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means telecoms can use data-driven insights to can monitor the state of equipment, predict failure based on patterns, and proactively fix problems with communications hardware, such as cell towers, power lines, data centre servers, and even set-top boxes in customers’ homes.

In the short-term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with business demands. An innovative solution by AT&Tis using AI to support its maintenance procedures: the company is testing a drone to expand its LTE network coverage and to utilize the analysis of video data captured by drones for tech support and infrastructure maintenance of its cell towers.Preventive maintenance is not only effective on the network side, but on the customer’s side as well.Dutch telecom KPN analyses the notes generated by its call centre agents, and uses the insights generated to make changes to the interactive voice response (IVR) system.

Virtual Assistants

Conversational AI platforms — known as virtual assistants — have learned to automate and scale one-on-one conversations so efficiently that they are projected to cut business expenses by as much as $8 billion in the next five years. Telecoms have turned to virtual assistants to help contend with the massive number of support requests for installation, set up, troubleshooting and maintenance, which often overwhelm customer support centre Using AI, telecoms can implement self-service capabilities that instruct customers how to install and operate their own devices.

Vodafone introduced its new chatbot — TOBi to handle a range of customer service-type questions. The chatbotscales responses to simple customer queries, thereby delivering the speed that customers demand. Nokia’s virtual assistant MIKA suggests solutions for network issues, leading to a 20% to 40% improvement in first-time resolution.

Robotic process automation (RPA)

CSPs all have vast numbers of customers and an endless volume of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecommunications functions by allowing telecoms to more easily manage their back office operations and the large volumes of repetitive and rules-based processes. By streamlining execution of once complex, labor-intensive and time-consuming processes such as billing, data entry, workforce management and order fulfillment, RPA frees CSP staff for higher value-add work.

According to a survey by Deloitte, 40% of Telecom, Media and Tech executives say they have garnered “substantial” benefits from cognitive technologies, with 25% having invested $10 million or more. More than three-quarters expect cognitive computing to “substantially transform” their companies within the next three years.

Summary

Artificial intelligence applications in the telecommunications industry is increasingly helping CSPs manage, optimize and maintain not only their infrastructure, but their customer support operations as well. Network optimization, predictive maintenance, virtual assistants and RPA are examples of use cases where AI has impacted the telecom industry, delivering an enhanced CX and added value for the enterprise overall.