Monthly Archives: July 2016

Teach for change

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Passionate Foundation’s English Programme –Teach For Change- is aimed at supporting English language and Leadership competencies in Government High school students. The key elements of the initiative are Volunteer Training and Teacher Training. Teach For Change has been working since 2014 with volunteers and Government High school teachers to enhance their English teaching and Leadership skills

Teach For Change volunteers commit to two hours of classroom teaching every weekend. Each volunteer is assigned a class in a Government school. The volunteer over the period of one academic year transacts the modules developed by Teach For Change. The modules focus on The English Language and building leadership competencies in Government High School students.

Volunteers work relentlessly over one academic year to touch the lives of the students and transform their lives. The journey is not only transformative for the students but also for the volunteers who experience the joy of teaching and thier work to bridge the divide in our country.

The Teach For Change Program is active in one hundred government high schools in Hyderabad (Telangana), ten government schools in Vijayawada (Andhra Pradesh), ten government schools in Chennai (Tamil Nadu) and ten government schools in Bengaluru (Karnataka). Our success has been far reaching and now we seek to take this to the next level.
» Screening and Selection
» Training
» School Allotment
» Teaching and Assessment of Impact
» Review and Support

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Astricon 2016: Speaker Bhagvan Kommadi

Architect Corner update:
Astricon 2016, September 27-29, 2016 in Glendale Arizona

https://astricon2016.sched.org/event/7Zkk/voice-to-text-intelligent-knowledge-assistant

astricon

Speech Processing consists of speech coding, synthesis, recognition and speaker recognition modules. Speech Types can be isolated words, connected words,continuous and spontaneous speech. Speaker models can be dependent and independent. Independent models recognises the speech patterns of a large group of people. Dependent models are more accurate for the particular speaker.

Speech Recognition involves Audio recording and conversion of speech to text. Audio Recording are archived into a file system or a database. Recording and conversion might happen offline. Online sync and updates will happen when the connectivity exists. Accuracy of conversion is important. Pronunciation and Ascents are known challenges.

Speech Recognition

Audio recorded need to be classified into voiced or unvoiced sounds. The classification is done into silence/unvoiced/voiced sounds. The stop consonant identification and end point detection for isolated utterances is classified. Noisy environment will have unwanted signals and back ground.


The communicated speech in the audio can be classified into words, phrases and sentences by applying grammar. The converted text might be interpreted as words and phrases. In India, the language can be a mix of different dialects/native languages. The total number of languages is 1652 dialects/native languages.

 

Speech Processing consists of speech coding, synthesis, recognition and speaker recognition modules. Speech Types can be isolated words, connected words,continuous  and spontaneous speech. Speaker models can be dependent and independent. Independent models recognises the speech patterns of a large group of people. Dependent models are more accurate for the particular speaker.

Speaker diarisation is the process of partitioning an input audio stream into homogeneous segments according to speaker identity. Readability of an automatic speech transcription is improved when speaker’s true identity is provided.

The vocabulary is classified as small, medium, large, very large and out of vocabulary. Small vocabulary is ten of words. Medium vocabulary is hundreds of words. Large is thousands of words. Very Large is tens of thousands of words. Out of Vocabulary is mapping into the unknown word.

Speech systems have other important characteristics like environment variability, channel variability, speaker style, sex, age, speed of speech etc.,

Speech Perception consists of message understanding, language translation and feature extraction. Speaker recognition system is performed in four stages : analysis, feature extraction, modelling and testing.

The system will have dictionary based on dialect and language. The words with correct pronunciation will identified from the set of words available in the dictionary. The dictionary will be created with adjectives, pronouns, nouns and verbs. The updates for the dictionary will be supported on a regular basis.

Template based isolated word recognition, continuous speech  recognition and applying neural network to speech recognition are the other approaches applied for recognition.

The correctness of pronunciation and grammar in phrases and sentences is the key challenge in identifying the words in the audio with dictionary of words. The accuracy will be dependent on Read vs spontaneous speech. Adverse conditions while recording the audio will impact the accuracy of conversion and recording audio.

On Screen editing with suggestions for corrections will be provided for the decoded audio after identifying the words from dictionary of words.

Speech verification is verifying the correctness of pronounced speech. The expected speech to be pronounced is compared against the decode speech. The correctness of the speech is measured.

Speech analytics are measured based on topics being discussed, emotional character of the audio, locations of speech vs non-speech and periods of silence.

 

DevCon5 Speaker: Bhagvan Kommadi

check out : Speaking at Dev Con 5 on Aug 1-4 NYU, USA : “Big Data Traps” https://lnkd.in/edKzPPB

Dev5ConConf

The “big data” projects often give CXOs short-lived confidence about information they are gathering. As a result, precious time and resources are wasted chasing wrong targets and missed business opportunities.No doubt, Big data provides the necessary insight to decision making, but without great analytics and understanding of enterprise structure, this will also end up as another wild chase.

To start with, Big data author Nate Silver points out that no matter how much data you collect, the objective truth that comes from the data pretty much stays the same. Yves Morieux calls the “shadow of the future”—in this case, that entering inaccurate data negatively affects the overall customer experience.

The classic Traps shared by CXO’s are:

You can’t manage what you don’t measure

Meaningless metrics (just because you can measure something doesn’t mean you should manage it.)

Ability to process huge amounts of data means success and win in business

  • Big data is a black box
  • Big data can predict the future
  • You are wrong, big data results are correct
  • chief executive data analyser effect
  • Data does not make decisions, people do
  • There’s no magical end point, but big data based decision making is  related to continuous improvement and iterative decision making
  • NO  Key performance indicators

Role of Metrics:

The person analyzing the data should be the person who best understands the context and source of the data. (sales team can analyze salesforce data).Differentiation in the market brings out success – not blind copying  the market activities and trends. Need to have knowledge what is happening in the field is important. Identifying key process areas (KPA) and for each KPA, identify key performance indicators. Analytics can take your decision making to a specific limit and distance on time.

eco-chamber effect : which is when an expected outcome is reinforced throughout the process.

Data Relationship

False Positives

there is the problem of “false positives.” If we look at 200 variables and the relationships between them, we have 40,000 possible relationships. That will inevitably mean a lot of correlations which are statistically significant but in fact random.The classic case here is the “ Ashes Stock Market Predictor.” It holds that if if england teamwins, the market will rise in that year. If Australia team wins, the market will fall.. But it was clearly a statistical fluke. This particular case  is too obvious to fool anyone, but other accidental correlations will be more subtle, and we will not waste a lot of time getting excited about equally spurious phenomena.

Organizations now using big data, and actively putting it to use, this relationship is in the middle of a reboot. Not only that, but the future of big data allows you to understand the larger trends in the markets in your industry, helping you better plan out the future of your organization.

Organizations will also use big data to better understand how to analyze and manipulate trends for their day-to-day business. For example, according to Forbes, Delhaize America is already using big data “to study the impact of local weather on store and category sales,” showing them that warmer weather increases the purchase of magazines, while decreasing the purchase of certain grilling meats.

Quality of Big data:

  • People, processes, and technology
  • Validity, the degree to which the data conforms to logical criteria
  • Completeness, the degree to which the data required to make decisions, calculations, or inferences is available
  • Consistency, the degree to which data is the same in its definition, business rules, format, and value at any point in time
  • Accuracy, the degree to which data reflects reality
  • Timeliness, the degree to which data reflects the latest available information

“zero-touch processing.”

It may not be possible or economical to fix all data-quality issues, such as those associated with external data, at the source. In such cases, companies could employ middleware that effectively translates “bad data” into “usable data.” As an example, often the structured data in an accounts-payable system does not include sufficient detail to understand the exact commodity being purchased. Is an invoice coded “computing” for a desktop or a laptop? Work-arounds include text analytics that read the invoice text, categorize the purchase, and turn the conversion into a rule or model. The approach can be good enough for the intended uses and much more cost effective than rebuilding an entire enterprise-software data structure.

Big data accuracy

  • Asking the right questions
  • Evaluating data quality
  • Comparing assumptions with reality
  • Understanding factors can skew results and backing predictions with statistics
  • Heeding privacy issues
  • Combining more than one data source
  • Communicating findings in a meaningful way

Big data often consists of “found” data, rather than data you purposely go out to collect. Relying on found data may not include variables that play a role in the results,

Big data framework

  •   Hypotheses about the decisions you’re making and the analysis you’re looking to perform in order to make them.
  • Descriptions of the data needed to feed the desired analysis, and their sources.
  • An understanding of the gaps – areas where you don’t currently have data – and how you plan to fill them and make decisions in the meantime.

Big data frameworks have evolved to support large data processing, parallelising the data tasks, data cleansing and storage of data. Reporting and analytics related capabilities have been transformed to decision making, forecasting and predicting tools.

Big data platform has discovery and prediction capabilities.  Discovery consists of Clustering, Outlier detection and affinity analysis. Clustering is detecting natural groupings. Outlier detection is detecting anomalies. Affinity analysis is identifying co-occurrence patterns. Classification, Regression, Recommendation is part of Prediction process. Classification is predicting categories. Regression is predicting value. Recommendation is predicting preferences.

Recommendations need to be backed up with sound judgement.

Businesses need to have a “revenue-driven” or “risk management-driven” business case for using big data.

Revenue Driven use Cases in Banking

Big data use cases in financial services are capital  market trading analysis, retail banking customer management, risk management, compliance, regulations, predictive analytics, customer retention, churn analysis, social graph analysis, marketing campaign analysis, fraud detection and network monitoring.

Consider the uses for data in terms of technical, organisational, and data stewardship feasibility. Also look at how the data use fits in with the company’s existing project portfolio.

Other Factors – To look into

   Bigger is Not Necessarily Better

Even though big data allows for the collection of masses of information, only a small percentage of that information is actually useful.

   Quantity Does Not Mean Quality

Having enormous piles of data where the data is supposed to represent “all” can end up skewing results. A case in point comes from social media, where every tweet may be collected and used to gauge overall public sentiment or mood. This method automatically fails the accuracy test as Twitter users do not represent the entire population. Many rarely tweet while others may have never even set up accounts.

   ‘Found’ Data is Not Always Truly Accurate Data

Big data often consists of “found” data, rather than data you purposely go out to collect. Relying on found data may not include variables that play a role in the results, such as credit rating agencies reporting firm facts on mortgages based only on data they collected at a time when the real estate market was soaring.

    Data Based on Behaviors Can be Misleading

Basing conclusions on past behaviors can be risky, particularly if you’re not sure what caused those behaviors. Here the errors don’t necessarily come from the data itself, but rather the interpretation of the data. This can be especially dangerous when people insist they “have the numbers to prove” whatever notion they’re touting.

Challenges for Enterprises

Enterprises face challenges based on their size, profile, dependency on its correspondents,provision for correspondent  services and capabilities. Performance and Breakdown of big data solutions are the bottleneck points. Enterprises have traditionally faced complexity in handling data sizes,scale, extent, speed,efficiency,complexity and different formats.

Emerging types of partnerships and vendors especially in banking and telecom are broad based programs driven by cross selling with telcos and retailers and banks working with nimbler, local and regional vendors.Customer demand for faster and efficient payments, entry of non-banks and convergence of channels are the emerging trends in banking business model.

The challenges faced in any vertical industry are bringing the channels together and handling big data from complex multi channel service environments.

Regulations, Privaycy, Ethics, Risk Management, Counter party risk management and Treasury technology compliant with corporate IT standards are the challenges for Enterprise to adopt Big Data Frameworks.

Best Practices

The best practices followed during big data analysis are evolution of a legacy big data environment, having sandbox and production box, backup and archiving, having multiple cache for increasing latency, master data management and data cleansing.

Enterprises need to centralize data into a single high-quality, on-demand source using a “one touch” master-data collection process. (MDM)

Enterprises need to have a pilot program in advanced analytics to act as an incubator for developing big-data capabilities in its business units and creating a path to additional growth.

Within a Business uint, Big data prototyping need to be on public cloud as it can be scaled instantly. After prototyping is done, big data solution is moved to private cloud. Boundary crash can be avoided by implementing far limits on scalability. Streaming data analytics are implemented for specific applicable cases.

Data world is modelled by dividing the data into dimensions and facts. Separate Data are integrated using separate data sources. Structured and unstructured data are integrated. Name valued pair data sets are stored in no sql data sources.

Big data governance consists of data quality, metadata management, master data management, privacy, security and compliance. IT need to work with management and support the cross-organisational cooperation. Private data need to be secured and shared data will be shared to third parties, vendors, institutions and other enterprises. Roles are identified within enterprise for data stewards, sponsors, program drivers and users.

Assign a business owner to data. Data must be owned to become high quality. Companies can’t outsource this step. Someone on the business side needs to own the data, set the pace of change, and have the support of the C suite and the board of directors to resolve complex issues.

Enterprise Adoption & Overcoming Challenges & Traps

Enterprises are enhancing their capabilities for establishing data gathering and assembly guidelines, guidelines for external data sharing, data security privacy, alignment of new product releases with customer preferences, expertise to solve big data analysis and performance data analysis.

Enterprises are focussing on big data initiatives towards tactical business objectives, product information management,  performance management, business execution correction, innovation through new products and predictive capabilities.

Big Data Platform

A Big data platform has operational services, data services  and  enterprise readiness services to provide High availability, Disaster Recovery, Security. Data Visualisation apps and Business Tools use Textual analysis, predictive analysis and statistical analysis services. Data Acquisition, Data Refinement data integration and data management is part of Data services layer. Semistructured Data analysis, Structured and Unstructured Data and Syndicated Data is part of Data Repository Layer.

 

Fly Dubai Hackathon

Architect Corner update : Bhagvan Kommadi Judge at Fly Dubai hackathon https://lnkd.in/fjNU4t5

flydubai

check out photos: https://lnkd.in/fyhFE3j

all

In a hackathon, teams will participate and code for a theme. The api is provided and technology stack is chosen by the team. Judging criteria will be as below:

  • Simplicity:  The demo needs to solve a real life problem specific to the theme. The idea is to reduce coding unwanted things and loose focus through the hackathon.
  • Design: The design (UI and backend) needs to be attractive and specific to the problem. Most of the time developers are pushed to focus on tech aspect and use open source libraries to build a good looking UI.
  • Technology: Technology is the enabler for change. Functionality is also an important factor
  • Originality/Novelty : The idea needs to be originial and novel.
  • Complexity: The problem needs to be complex and difficult to tackle. The solution needs to address the key aspects of the problem
  • WoW factor: Is there any thing awesome in your demo to please the business and technology users.
  • Non Functional Architectural aspects: Security, Availability, Scalability and FailOver needs to be considered while choosing the stack and deployment architecture

Mentoring Startups

Nasscom 10000 startups- mentor panel

https://lnkd.in/fBNNeDu

Mentoring startups – 10000 startups & Ask Mentor

https://lnkd.in/fd2cxGF

10k

“10,000 Startups” is a vision, which is committed to incubate, fund and provide ambient support to impact 10,000 technology startups in India, by 2023. The aim is to nurture the  startups into full-fledged technology stalwart  companies, by giving them support via access to startup incubators, accelerators, angel investors, venture capitalists, startup support groups, mentors, and technology corporations.

askmentorAskMentor is built as a platform to facilitate networking between mentors and mentees. It is a professional social network matching entrepreneurs to the advice and resources to increase the odds of success. We believe mentorship is a key ingredient in the path towards success in one’s personal and professional lives.

Teach For Change Initiative

Architect Corner update: Teach for change
www.linkedin.com/hp/update/6160114172068761600

 

Teach  For Change 

The vision is to build a network of volunteers to transform our Government schools and provide high quality education for all, regardless of their family’s income.The objective of the initiative is to improve the overall literacy standards at Government schools. As part of the initiative, TFC’s dedicated volunteers teach Leadership Skills and the English Language, at primary and high school levels in Government schools.

  • To conduct the processes of education and teaching in accordance with social, cultural, moral and ethical expectations of families and society.
  • To undertake and conduct the learning process of students using procedures such as planning, teaching, monitoring, developing syllabi, innovating in order to achieve the results established by the education system.
  • Take part in the development of the School’s Institutional Project through discussions, decision making, working with other teachers to encourage their view of teaching and learning as a single process.