All posts by Neil Harwani

Interested in movies, music, history, computer science, software, engineering, management and technology

Web portal & commerce cyber forensics

For this discussion, we will refer the top open-source products like Liferay, Drupal, WordPress, etc. and one proprietary portal like SharePoint which has good documentation.

Before studying cyber forensics for portals and commerce area, we must understand it’s architecture and security.

Web application architecture:

  • Three tier architecture:
  • CDN, WAF, Web server – Typically in external exposed subnet – Demilitarized subnet / zone
  • Application Server, Database, File Store, Search, Caching in internal subnet – Militarized zone
  • Integrations like IAM/LDAP/SSO, APIs, LLMs, AI, MQ, Kafka, etc. from various layers possible
  • Server / cloud / VM infrastructure / VPN
  • Use-cases:
  • Insurance policy administration
  • Supplier portals
  • Intranets
  • Search based use cases
  • Workflows / BPMs
  • eCommerce
  • Public websites and more
  • Deployment:
  • Cloud
  • In-prem / self-hosted
  • Clustered environment at most layers

Solutions could be monolith or micro-services driven, etc.

Security:

  • Programming level
  • Secure programming around APIs, Integrations and more
  • App server security
  • Separate subnets
  • JVM security
  • Web server & overall security
  • Https
  • CSP
  • CSRF / CORS
  • XSS
  • Server hardening
  • Access / IAM / 2FA / MFA
  • OWASP like SQL injection and more
  • Cookies & Sessions
  • DoS, DDoS, Malware, Spyware, etc.
  • And more – Security – Liferay Learn
  • Products:
  • Liferay
  • Drupal
  • WordPress
  • SharePoint, Mozilla foundation and many more
  • Custom portals, commerce built with PHP, Java, Dot Net and more

Forensics:

  • Logs of app server
  • Logs of web servers – Why? – IPs many times don’t pass beyond this layer of CDN, WAF, Web server
  • Logs of CDN, WAF
  • Logs of cloud, infra, VM, etc. and details Network Management System, Application Performance Monitoring
  • Database for the state – Very critical – don’t forget this if you get access to logs and overall access of portal
  • File store
  • Search
  • Code for integration, customizations
  • Configurations – XMLs, etc.
  • Access logs and full control of all servers
  • DNS pings
  • Integration logs
  • Concerns: PII, Privacy, State of workflows, system, data, content, etc. Multi session login by single user and 2FA/MFA
  • Building chain of events
  • Audit trails if enabled
  • Admin and other rights
  • Data governance, data security, data analytics, web analytics like Google Analytics
  • Logins, Logouts, Public APIs, Insecure APIs, Insecure servers, Authentication, Authorization
  • Understanding the resolution path: User -> ISP -> Internet over https -> DNS resolution -> Portal CDN -> WAF -> WS (External world and https typically breaks here) -> AS -> Integrations & Search -> DB and back it goes

References:

Possibilities for AI powered features in Liferay – Part 1 via ChatGPT & Gemini

A good list on possibilities for AI powered features in Liferay – Part 1 from GAI/LLMs. Sharing here for everyone’s benefit. Came across during talking to ChatGPT and Gemini, very good ideas – some are inbuilt, some need customizations. Possibilities are endless.

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Liferay, a digital experience platform (DXP), integrates various AI-related features to enhance user experience, streamline content management, and provide advanced analytics. These features can be built into the platform or extended using AI tools and frameworks. Here’s an overview of AI-related capabilities in Liferay:

### 1. AI-Powered Search

Intelligent Search Recommendations:

– Uses machine learning to rank search results based on user behavior, content relevance, and personalization.

Natural Language Processing (NLP):

– Allows for semantic search by understanding the intent behind search queries.

Autocomplete and Suggestions:

– Dynamically suggests search terms as users type, improving discoverability.

### 2. Content Recommendations

Personalized Content Delivery:

– Machine learning algorithms analyze user behavior to recommend content that matches individual preferences.

Dynamic Segmentation:

– Uses AI to create audience segments based on real-time user interactions and historical data.

Content A/B Testing:

– Automated analysis of content performance to determine optimal configurations for target audiences.

### 3. AI-Driven Analytics

Predictive Analytics:

– Forecasts user trends and behaviors to optimize marketing campaigns and content strategies.

Customer Journey Analysis:

– Uses AI to identify bottlenecks and opportunities in user pathways, helping improve user experience.

Sentiment Analysis:

– NLP tools analyze user feedback, comments, and interactions to gauge customer sentiment.

### 4. Chatbots and Virtual Assistants

Conversational AI:

– Integrates chatbots for automated customer support and query resolution using AI-powered frameworks like Dialogflow or Rasa.

Multilingual Support:

– NLP models enable real-time translation and language processing, providing support in multiple languages.

Integration with Workflow Automation:

– Chatbots can trigger Liferay workflows for tasks like form submission, user registration, or ticket generation.

### 5. Image and Media Processing

AI-Powered Image Tagging:

– Automatically tags and categorizes uploaded images using AI-based image recognition tools.

Video Transcription and Analysis:

– Integrates AI tools for video transcription, keyword extraction, and content indexing.

Facial Recognition:

– Can be used in personalized content delivery, e.g., identifying user personas through uploaded images.

### 6. Content Creation Assistance

AI Writing Tools:

– Integrates with tools like OpenAI’s GPT or custom NLP models to suggest text, improve grammar, or automate content generation.

Content Summarization:

– AI summarizes long-form content, making it easier to consume or repurpose for different channels.

Translation Services:

– Uses AI-driven services (e.g., Google Translate, Azure Translator) to localize content for multilingual audiences.

### 7. Workflow Automation

Automated Decision-Making:

– AI models can analyze workflow data and suggest optimizations or auto-complete certain steps.

Intelligent Approvals:

– AI assesses workflow conditions to approve or reject tasks, reducing manual intervention.

### 8. Security and Fraud Detection

Anomaly Detection:

– AI identifies suspicious user activity, such as login attempts or data breaches.

Adaptive Authentication:

– AI monitors user behavior for dynamic risk assessment and adjusts authentication requirements accordingly.

### 9. Search Engine Optimization (SEO)

Automated Metadata Generation:

– AI analyzes content and suggests metadata (titles, descriptions, tags) to optimize search rankings.

Keyword Optimization:

– Identifies trending keywords and suggests their integration into content.

### 10. AI Framework Integration

– Liferay supports integration with third-party AI frameworks and APIs, such as:

TensorFlow and PyTorch: For custom machine learning models.

OpenAI GPT: For advanced conversational AI or content generation.

Google AI/ML Services: For vision, NLP, and analytics.

### 11. Multichannel Experience

Adaptive UI/UX:

– AI analyzes user interaction patterns to adjust layouts, suggest UI optimizations, or adapt experiences across devices.

Voice Search and Commands:

– Enables voice-based interaction for content discovery and navigation.

### 12. Predictive Content Scheduling

– AI predicts the optimal time to publish or update content based on user engagement trends, maximizing visibility and impact.

### How Liferay Integrates AI

Out-of-the-Box Features:

– Some AI-powered features are available directly in Liferay DXP.

Customizable via APIs:

– Developers can integrate AI tools into Liferay using REST or GraphQL APIs.

Marketplace Plugins:

– Liferay Marketplace offers plugins and extensions that enhance AI capabilities (e.g., chatbots, recommendation engines).

### Conclusion

Liferay’s AI features provide powerful tools for personalizing user experiences, automating workflows, and enhancing content management. By integrating with advanced AI frameworks and tools, Liferay enables businesses to stay competitive in a data-driven, customer-focused digital landscape.

********************************************

Liferay DXP is a powerful digital experience platform that integrates AI capabilities to enhance content creation, streamline operations, and deliver context-intelligent assistance. Here are some of the key AI-related features in Liferay:

Content Creation:

  • AI-powered content generation: Liferay DXP 2023.Q4+ and Portal 7.4 GA98+ integrate with OpenAI’s ChatGPT engine to generate text content within the Web Content editor. This allows users to create content more efficiently by providing prompts and letting the AI generate relevant text.
  • AI-powered image generation: Liferay also supports AI-powered image generation, enabling users to create custom images based on text descriptions. This can be useful for creating unique visuals for blogs, articles, or marketing materials.

Content Management and Personalization:

  • AI-powered semantic search: Liferay’s search functionality can leverage AI to understand the semantic meaning of search queries, allowing for more accurate and relevant search results.
  • Intelligent content recommendations: AI can analyze user behavior and preferences to recommend relevant content, improving user engagement and satisfaction.
  • Personalized user experiences: AI can be used to personalize user experiences based on their individual needs and preferences, such as tailoring content, layouts, and notifications.

Other AI-powered Features:

  • Auto-tagging with Google Cloud AI: Liferay can automatically tag content with relevant keywords using Google Cloud Vision AI and Cloud Natural Language AI, streamlining content categorization and discovery.
  • AI-powered insights: Liferay can provide AI-powered insights to help organizations make better decisions. For example, AI-powered sales forecasts can help sales and finance teams estimate revenues and identify growth opportunities.

By leveraging AI, Liferay DXP empowers organizations to create more engaging and personalized digital experiences, automate tasks, and gain valuable insights from their data.

Performance Tuning in Liferay – Part 4

In addition to 3-part series on performance tuning given here below are additional points to explore for performance tuning in Liferay.

  • Java 17/21
  • Network and subnet between Application server, Web server, Elastic search, Database
  • Difference between JMeter and JVM/Glowroot load times for pages
  • Lighthouse reports
  • Elastic search response times and tuning
  • Fragment caching
  • Logging levels for various components
  • Cache busting / documents & media caching parameters
  • Integrations especially web analytics
  • DNS/IP resolution time
  • Hardware especially hyper threading and such on VM/Cloud/Private cloud
  • TCP & Web.xml / ElasticSearch.yml / JVM.Options.d
  • Filestore type
  • vCPU to thread to physical CPU / core mapping
  • RAM / harddisk / NFS type for filestore
  • Traffic shaping in the deployment done or not
  • Finding out the true capacity of app server by loading the app server from the same subnet
  • Disabling / enabling relevant servlet filters
  • Replication, backup, syncs, anti-virus, IPS/Firewall latency analysis
  • Analysis of vertical vs. horizontal scaling by experimentation / performance testing

Keywords from Calculus

Comprehensive List of Topics in Calculus:

  1. Limits and Continuity
  2. Limits of Functions
  3. One-Sided Limits
  4. Limit Laws
  5. L’Hôpital’s Rule
  6. Continuity and Discontinuity
  7. Intermediate Value Theorem
  8. Infinite Limits
  9. Limits at Infinity

Differential Calculus

  1. Derivatives
  2. Rules of Differentiation
  3. Chain Rule
  4. Product Rule
  5. Quotient Rule
  6. Implicit Differentiation
  7. Higher-Order Derivatives
  8. Derivatives of Trigonometric Functions
  9. Derivatives of Exponential Functions
  10. Derivatives of Logarithmic Functions
  11. Derivatives of Hyperbolic Functions
  12. Inverse Function Theorem
  13. Mean Value Theorem
  14. Rolle’s Theorem
  15. Taylor and Maclaurin Series
  16. Linear Approximation
  17. Differential Equations (First-Order)
  18. Newton’s Method
  19. Optimization Problems
  20. Related Rates
  21. Curvature and Radius of Curvature
  22. Concavity and Points of Inflection
  23. Asymptotes and Limits
  24. Critical Points
  25. Maximum and Minimum Values
  26. Applications of Derivatives

Integral Calculus

  1. Antiderivatives
  2. Indefinite Integrals
  3. Definite Integrals
  4. Riemann Sums
  5. Fundamental Theorem of Calculus
  6. Techniques of Integration
  7. Integration by Parts
  8. Partial Fraction Decomposition
  9. Trigonometric Integrals
  10. Trigonometric Substitution
  11. Improper Integrals
  12. Integration by Substitution
  13. Numerical Integration (Simpson’s Rule, Trapezoidal Rule)
  14. Integration of Rational Functions
  15. Gamma and Beta Functions
  16. Area Under Curves
  17. Volume of Solids of Revolution
  18. Arc Length
  19. Surface Area of Revolution
  20. Average Value of a Function
  21. Work and Energy Problems
  22. Center of Mass and Centroids
  23. Moments of Inertia
  24. Probability Density Functions (PDF)
  25. Applications of Integration

Multivariable Calculus

  1. Partial Derivatives
  2. Chain Rule for Partial Derivatives
  3. Directional Derivatives
  4. Gradient Vector
  5. Divergence and Curl
  6. Lagrange Multipliers
  7. Multiple Integrals (Double and Triple Integrals)
  8. Change of Variables (Jacobian)
  9. Cylindrical and Spherical Coordinates
  10. Surface Integrals
  11. Line Integrals
  12. Green’s Theorem
  13. Stokes’ Theorem
  14. Divergence Theorem
  15. Laplacian and Harmonic Functions
  16. Scalar and Vector Fields
  17. Vector-Valued Functions
  18. Tangent and Normal Vectors
  19. Curvilinear Coordinates
  20. Parametric Surfaces and Curves

Series and Sequences

  1. Convergence and Divergence of Sequences
  2. Series and Partial Sums
  3. Geometric Series
  4. Harmonic Series
  5. Power Series
  6. Taylor Series
  7. Maclaurin Series
  8. Radius and Interval of Convergence
  9. Alternating Series
  10. Absolute and Conditional Convergence
  11. Ratio and Root Tests
  12. Comparison Test
  13. Integral Test
  14. P-Series
  15. Binomial Series
  16. Fourier Series
  17. Uniform Convergence
  18. Complex Series

Vector Calculus

  1. Vector Fields
  2. Dot Product
  3. Cross Product
  4. Scalar and Vector Projections
  5. Gradient, Divergence, and Curl
  6. Line Integrals of Vector Fields
  7. Surface Integrals of Vector Fields
  8. Path Independence and Conservative Fields
  9. Potential Functions
  10. Flux and Circulation
  11. Conservative Fields
  12. Helmholtz Decomposition
  13. Irrotational and Solenoidal Fields

Differential Equations and Advanced Topics

  1. Ordinary Differential Equations (ODEs)
  2. Partial Differential Equations (PDEs)
  3. Separation of Variables
  4. Fourier Transform and Laplace Transform
  5. Eigenvalues and Eigenfunctions
  6. Bessel Functions
  7. Legendre Polynomials
  8. Sturm-Liouville Theory
  9. Nonlinear Differential Equations
  10. Systems of Differential Equations
  11. Stability and Phase Portraits
  12. Boundary Value Problems
  13. Green’s Functions

Key Topics for Random Processes & Statistics and Probability

Comprehensive List of Topics for Random Processes:

1. Stochastic Processes

2. Markov Chains

3. Continuous-Time Markov Chains

4. Markov Decision Processes (MDPs)

5. Random Walks

6. Poisson Processes

7. Renewal Processes

8. Stationary Processes

9. Weak and Strong Stationarity

10. Autocorrelation Function

11. Autoregressive Processes (AR)

12. Moving Average Processes (MA)

13. ARMA and ARIMA Models

14. ARCH and GARCH Models

15. Ergodicity

16. Brownian Motion (Wiener Process)

17. Fractional Brownian Motion

18. Gaussian Processes

19. Lévy Processes

20. Martingales

21. Submartingales and Supermartingales

22. Random Fields

23. Spectral Analysis of Time Series

24. Power Spectral Density

25. Cross-Correlation and Cross-Spectrum

26. Queuing Theory

27. Random Walk Hypothesis

28. Mean Reversion

29. Wiener-Khinchin Theorem

30. Entropy and Information Theory

31. Fokker-Planck Equation

32. Kolmogorov Equations

33. Jump Processes

34. Semi-Markov Processes

35. Diffusion Processes

36. Stochastic Differential Equations (SDEs)

37. Ito’s Lemma

38. Langevin Equation

39. Filtering Theory (e.g., Kalman Filter)

40. Random Measures

41. Cox Processes

42. Birth-Death Processes

43. Time Series Analysis

44. Hidden Markov Models (HMM)

45. Self-Similar Processes

46. Long-Range Dependence

47. Hawkes Processes

48. Empirical Processes

49. Random Matrices

50. Random Graphs

Comprehensive List of Topics for Probability and Statistics:

1. Basic Probability Theory

2. Axioms of Probability

3. Random Variables

4. Probability Mass Function (PMF)

5. Probability Density Function (PDF)

6. Cumulative Distribution Function (CDF)

7. Joint, Marginal, and Conditional Distributions

8. Expected Value (Mean)

9. Variance and Standard Deviation

10. Covariance and Correlation

11. Skewness and Kurtosis

12. Moments and Moment Generating Functions

13. Chebyshev’s Inequality

14. Probability Generating Functions

15. Characteristic Functions

16. Law of Large Numbers

17. Central Limit Theorem

18. Convergence in Probability and Distribution

19. Bayes’ Theorem

20. Bayesian Inference

21. Prior and Posterior Distributions

22. Hypothesis Testing

23. p-Values

24. Type I and Type II Errors

25. Confidence Intervals

26. Sampling Distributions

27. Point Estimation

28. Maximum Likelihood Estimation (MLE)

29. Method of Moments

30. Bayesian Estimation

31. Interval Estimation

32. Sampling Theory

33. Markov Property

34. Monte Carlo Methods

35. Bootstrap and Resampling Methods

36. Permutation Tests

37. Experimental Design

38. Analysis of Variance (ANOVA)

39. Factorial Designs

40. Regression Analysis

41. Linear Regression

42. Multiple Linear Regression

43. Logistic Regression

44. Polynomial Regression

45. Generalized Linear Models (GLM)

46. Mixed-Effects Models

47. Time Series Analysis

48. Non-parametric Statistics

49. Parametric vs. Non-Parametric Tests

50. Goodness-of-Fit Tests (e.g., Chi-Square Test)

51. Multivariate Statistics

52. Principal Component Analysis (PCA)

53. Factor Analysis

54. Discriminant Analysis

55. Canonical Correlation Analysis

56. Clustering (K-means, Hierarchical)

57. Classification Techniques

58. Decision Trees

59. Random Forests

60. Support Vector Machines (SVM)

61. Naive Bayes Classifier

62. Bayesian Networks

63. Hidden Markov Models (HMMs)

64. Time Series Forecasting

65. AR, MA, and ARIMA Models

66. Seasonal Decomposition

67. Exponential Smoothing

68. Cointegration and Error Correction Models

69. Time-Varying Volatility Models (ARCH/GARCH)

70. Survival Analysis

71. Reliability Theory

72. Extreme Value Theory

73. Risk Analysis

74. Quality Control and SPC

75. Experimental Design and RCTs

76. Empirical Bayes Methods

77. Robust Statistics

78. Statistical Learning Theory

79. Bootstrap Confidence Intervals

80. Empirical Likelihood

81. Kernel Density Estimation

82. Probability Inequalities (e.g., Jensen’s Inequality)

83. Asymptotic Theory

84. Sequential Analysis

85. Influence Functions

86. U-statistics

87. Sufficient Statistics

88. Exponential Families

89. Decision Theory

90. Game Theory

91. Utility Theory

92. Meta-Analysis

93. Statistical Computing

94. Missing Data Techniques

95. Spatial Statistics

96. Functional Data Analysis

97. Multilevel Models

98. Time-Varying Coefficient Models

99. Causal Inference

100. Propensity Score Matching

Notes on Entrepreneurship theories as per ChatGPT – Part 1

Entrepreneurship is a multifaceted field that intersects with economics, sociology, psychology, and strategic management. Over the years, researchers have developed a wide range of theories to explain various aspects of entrepreneurship, such as opportunity recognition, innovation, firm creation, strategic behavior, and more. Below is an exhaustive list of entrepreneurship theories along with brief descriptions of each:

### 1. Opportunity-Based Theories

1. Opportunity Recognition Theory:

Key Concept: Entrepreneurs identify and act on profitable opportunities that others do not see.

Major Contributor: Israel Kirzner (1973).

Core Idea: The theory distinguishes between alertness (recognizing opportunities) and discovery.

2. Entrepreneurial Opportunity Theory:

Key Concept: The process of identifying, evaluating, and exploiting business opportunities.

Major Contributors: Shane & Venkataraman (2000).

Core Idea: Entrepreneurship is defined as the nexus of two phenomena: the presence of lucrative opportunities and the presence of enterprising individuals.

3. Resource-Based Theory (RBT):

Key Concept: Entrepreneurial success is determined by the resources controlled by the entrepreneur.

Major Contributor: Jay Barney (1991).

Core Idea: Resources must be valuable, rare, inimitable, and non-substitutable (VRIN) to provide a sustainable competitive advantage.

4. Discovery Theory vs. Creation Theory:

Key Concept: Entrepreneurs either discover pre-existing opportunities (discovery theory) or create new opportunities through innovation and interactions (creation theory).

Major Contributors: Sarasvathy (2001) and Alvarez & Barney (2007).

Core Idea: Discovery theory assumes an objective reality, whereas creation theory views opportunities as emerging from human action.

### 2. Economic Theories of Entrepreneurship

1. Schumpeterian Theory (Creative Destruction):

Key Concept: Entrepreneurs are innovators who create economic growth through creative destruction, disrupting existing markets.

Major Contributor: Joseph Schumpeter (1934).

Core Idea: Innovation is the source of economic development, and entrepreneurs introduce new products, processes, and business models.

2. Knightian Uncertainty Theory:

Key Concept: Entrepreneurs make decisions under uncertainty and bear the associated risks.

Major Contributor: Frank Knight (1921).

Core Idea: Differentiates between risk (measurable probability) and uncertainty (unknown probability), with the entrepreneur rewarded for dealing with uncertainty.

3. Cantillon’s Theory of Entrepreneurship:

Key Concept: Entrepreneurs are risk-takers who buy at certain prices and sell at uncertain prices.

Major Contributor: Richard Cantillon (18th century).

Core Idea: The entrepreneur acts as a middleman and absorbs market risk.

4. Marshallian Demand-Supply Theory:

Key Concept: Entrepreneurship arises from economic forces where demand and supply create incentives for entrepreneurial activity.

Major Contributor: Alfred Marshall (1890).

Core Idea: Entrepreneurs allocate resources efficiently to balance demand and supply in markets.

5. Baumol’s Theory of Productive, Unproductive, and Destructive Entrepreneurship:

Key Concept: Entrepreneurs engage in activities that can be productive (value-creating), unproductive (rent-seeking), or destructive (illegal).

Major Contributor: William Baumol (1990).

Core Idea: The allocation of entrepreneurship depends on the institutional environment and rewards structure.

### 3. Behavioral Theories of Entrepreneurship

1. Psychological Traits Theory:

Key Concept: Focuses on the personality traits of entrepreneurs (e.g., risk-taking, need for achievement).

Major Contributor: David McClelland (1961).

Core Idea: High “need for achievement” drives individuals to become entrepreneurs.

2. Locus of Control Theory:

Key Concept: Entrepreneurs believe that they can control their own destiny (internal locus of control) rather than being influenced by external forces.

Major Contributor: Julian Rotter (1966).

Core Idea: Entrepreneurs with an internal locus of control are more likely to take initiative and innovate.

3. Self-Efficacy Theory:

Key Concept: A person’s belief in their own ability to execute tasks and achieve goals.

Major Contributor: Albert Bandura (1977).

Core Idea: High entrepreneurial self-efficacy correlates with greater likelihood of pursuing entrepreneurial opportunities.

### 4. Sociological Theories of Entrepreneurship

1. Network Theory:

Key Concept: Entrepreneurial success is influenced by social networks and the quality of personal and professional relationships.

Major Contributor: Mark Granovetter (1973).

Core Idea: Weak ties are crucial for accessing diverse information and resources.

2. Social Capital Theory:

Key Concept: Social capital, such as trust, norms, and networks, is crucial for entrepreneurial success.

Major Contributor: Pierre Bourdieu (1986).

Core Idea: Entrepreneurs leverage social capital to access resources and opportunities.

3. Ecological Theory of Entrepreneurship:

Key Concept: Entrepreneurship is influenced by social, cultural, and economic environments.

Major Contributor: Howard Aldrich (1979).

Core Idea: Explains entrepreneurship as an adaptive response to environmental conditions.

### 5. Strategic and Management Theories of Entrepreneurship

1. Strategic Entrepreneurship Theory:

Key Concept: Combines opportunity-seeking and advantage-seeking behaviors to create wealth.

Major Contributors: Hitt, Ireland, Camp, & Sexton (2001).

Core Idea: Entrepreneurs balance exploration and exploitation to achieve long-term success.

2. Dynamic Capabilities Theory:

Key Concept: Focuses on how firms adapt to changing environments through dynamic capabilities.

Major Contributor: Teece, Pisano, & Shuen (1997).

Core Idea: Entrepreneurs build dynamic capabilities to integrate, build, and reconfigure resources for competitive advantage.

3. Effectuation Theory:

Key Concept: Entrepreneurs start with available resources and focus on controlling the future rather than predicting it.

Major Contributor: Saras Sarasvathy (2001).

Core Idea: Entrepreneurs focus on what they can control rather than trying to predict uncertain outcomes.

4. Corporate Entrepreneurship/Intrapreneurship Theory:

Key Concept: Focuses on entrepreneurial behavior within large organizations.

Major Contributor: Gifford Pinchot (1985).

Core Idea: Intrapreneurs drive innovation and renewal within established firms.

### 6. Innovation Theories in Entrepreneurship

1. Disruptive Innovation Theory:

Key Concept: Disruptive innovations displace established technologies or products.

Major Contributor: Clayton Christensen (1997).

Core Idea: Focuses on how simpler, cheaper innovations disrupt markets.

2. Innovative Entrepreneurship Theory:

Key Concept: Emphasizes the role of innovation in entrepreneurship.

Major Contributor: Schumpeter (1934).

Core Idea: Entrepreneurs introduce new products, processes, or services that drive economic change.

### 7. Entrepreneurial Ecosystem Theories

1. Entrepreneurial Ecosystem Theory:

Key Concept: Entrepreneurship is influenced by a supportive ecosystem that includes actors, institutions, and policies.

Major Contributors: Isenberg (2010) and Stam (2015).

Core Idea: A robust ecosystem promotes entrepreneurial success through access to capital, talent, and networks.

2. Institutional Theory:

Key Concept: Institutions shape the entrepreneurial environment through formal and informal rules.

Major Contributor: Scott (1995).

Core Idea: Institutional support and barriers significantly influence entrepreneurial behavior.

### 8. Behavioral Decision-Making Theories

1. Prospect Theory:

Key Concept: Entrepreneurs make decisions based on potential gains and losses, which are weighed differently.

Major Contributors: Kahneman & Tversky (1979).

Core Idea: Entrepreneurs are more likely to take risks when faced with potential losses.

2. Bricolage Theory:

Key Concept: Entrepreneurs create solutions using limited resources at hand.

Major Contributor: Baker & Nelson (2005).

Core Idea: Entrepreneurs use “making do” strategies to address resource constraints creatively.

This comprehensive list covers the major entrepreneurship theories, providing you with a strong theoretical foundation for understanding the diverse aspects of entrepreneurial behavior, strategy.

Resources on Advanced Statistics & Probability as per ChatGPT

Here’s a list of some excellent resources across various formats—books, YouTube channels, and blogs—that cover advanced statistics and probability theories:

Books:

1. “Introduction to Probability” by Dimitri P. Bertsekas and John N. Tsitsiklis

– A comprehensive introduction to probability, available for free in PDF form on MIT’s OpenCourseWare.

– [Link to PDF](https://athenasc.com/probbook.html)

2. “Think Stats” by Allen B. Downey

– This book focuses on applying statistics to real-world data, with practical examples using Python.

– [Link to PDF](https://greenteapress.com/wp/think-stats-2e/)

3. “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

– A highly regarded book on statistical learning, offering a deep dive into many advanced topics.

– [Link to PDF](https://web.stanford.edu/~hastie/ElemStatLearn/)

4. “All of Statistics: A Concise Course in Statistical Inference” by Larry Wasserman

– A great resource that covers both basic and advanced topics in statistics.

– [Link to PDF](https://www.stat.cmu.edu/~larry/all-of-statistics/)

5. “Introduction to Statistical Thought” by Michael Lavine

– This book covers the foundations of statistical inference and is available for free online.

– [Link to PDF](https://www.math.umass.edu/~lavine/Book/book.html)

YouTube Channels:

1. MIT OpenCourseWare – Probability and Statistics

– Features lectures from MIT’s undergraduate and graduate courses, including advanced topics in probability and statistics.

– [MIT OCW YouTube Channel](https://www.youtube.com/user/MIT)

2. Khan Academy

– Although mostly known for basic statistics, Khan Academy also offers more advanced courses in probability and statistical inference.

– [Khan Academy – Probability & Statistics](https://www.youtube.com/user/khanacademy)

3. StatQuest with Josh Starmer

– Excellent channel that breaks down complex statistical concepts into easily understandable segments, including advanced topics.

– [StatQuest YouTube Channel](https://www.youtube.com/user/joshstarmer)

4. Brilliant.org

– While Brilliant offers paid content, their YouTube channel provides free videos covering advanced mathematical concepts, including probability and statistics.

– [Brilliant.org YouTube Channel](https://www.youtube.com/c/Brilliantorg)

5. Harvard University – STAT110 (Probability)

– Lectures from Harvard’s popular STAT110 course, taught by Professor Joe Blitzstein, covering probability theory in depth.

– [Harvard University – STAT110 YouTube Channel](https://www.youtube.com/playlist?list=PL2SOU6wwxB0v1kQTpqpuuGIjJRWJaFfeH)

Blogs and Online Courses:

1. Cross Validated (Stack Exchange)

– A Q&A site specifically for statistics, probability, and data science. It’s a great place to see advanced problems discussed in depth.

– [Cross Validated](https://stats.stackexchange.com/)

2. OpenIntro

– Offers free textbooks, labs, and resources on statistics, including advanced topics.

– [OpenIntro](https://www.openintro.org/)

3. Towards Data Science (Medium)

– A popular blog on Medium with numerous articles on advanced statistics, probability, and their applications in data science.

– [Towards Data Science](https://towardsdatascience.com/)

4. DataCamp Community

– While DataCamp offers paid courses, their blog has free articles and tutorials on advanced statistical methods.

– [DataCamp Community](https://www.datacamp.com/community)

5. Probability and Statistics EBook

– An online resource that provides detailed explanations of advanced topics in probability and statistics.

– [Probability and Statistics EBook](http://www.probabilitycourse.com/)

MOOCs and Online Lectures:

1. Coursera – Statistical Learning by Stanford University

– A free course that covers statistical learning, based on the book “The Elements of Statistical Learning.”

– [Coursera – Statistical Learning](https://www.coursera.org/learn/statistical-learning)

2. edX – Probability: The Science of Uncertainty and Data by MIT

– A free course that dives deep into probability theory and applications.

– [edX – MIT Probability Course](https://www.edx.org/course/probability-the-science-of-uncertainty-and-data)

3. Harvard Online Learning – Data Science: Probability

– Part of Harvard’s Data Science Professional Certificate, this course is available for free auditing.

– [Harvard – Data Science: Probability](https://online-learning.harvard.edu/course/data-science-probability)

These resources cover a broad range of advanced topics in probability and statistics, and they offer various levels of depth, from introductory overviews to rigorous academic treatments.

How to tame the SEO beast with Liferay? Part 1.

Here are some keywords and concepts to explore:

1. Performance tuning – https://www.linkedin.com/pulse/performance-tuning-liferay-part-3-neil-harwani-nsoof/

2. Performance options for pages & search in built in Liferay – -> https://learn.liferay.com/w/dxp/using-search/search-pages-and-widgets/search-insights

–> https://learn.liferay.com/w/dxp/content-authoring-and-management/page-performance-and-accessibility/analyze-seo-and-accessibility-on-pages

–> https://learn.liferay.com/w/dxp/content-authoring-and-management/page-performance-and-accessibility/about-the-page-audit-tool

3. SEO features in Liferay

–> https://learn.liferay.com/w/dxp/site-building/optimizing-sites

–> https://learn.liferay.com/w/dxp/site-building/displaying-content/using-display-page-templates/configuring-seo-and-open-graph

4. Set up your own monitoring (simple JMeter is a good start) and focus on page load times in conjunction with other things rather than only scores. Use various tools – an example: –> https://bloggerspassion.com/website-performance-speed-test-tools/

–> https://developer.chrome.com/docs/lighthouse/overview/

5. Liferay headless – https://www.liferay.com/solutions/headless-apis

6. Lazy loading, innovative solutions like lighter pages with type ahead and so on

7. Understand various tools for website performance, formula for page speed insights & SEO but focus on your metrics like page load speed, image quality. Don’t blindly pick a tool and follow it. However, suggestions / recommendations / insights of various tools should be explored and worked upon as needed

8. Note: Mobile score is throttled down by page speed insights to slow 3G or so

Other hints:

1. Robots.txt

2. Core web vitals

3. Sitemap

4. https

5. Broken links, friendly URLs, mobile friendliness

6. Content quality

7. Tags

8. Images

9. Caching

Liferay with right configuration, customization & tuning is capable of some magical things 🙂

Keywords from my FPM journey – Part 2

Some of the keywords from my FPM journey – Part 2:

Learnings from eMasters Data Science for Decision Making – IIT Gandhinagar – Part 1

Below are the learnings from eMasters Data Science for Decision Making – IIT Gandhinagar – Part 1. This needs to be an article in itself. You can find more details here about eDSDM here: e-Masters | IITGNX

  • There is Mathematics for your AIML problem in feature engineering, preprocessing, evaluation metrics, models, errors, etc. and so on or you can build some relationship between Mathematics and your problem either by breaking the problem into pieces or transforming data and so on.
  • Human intuition is still invaluable like in situations of imbalanced datasets, regression and more.
  • Visualization and EDA almost always help for your problems. For higher dimension problems you can do PCA, T-SNE, shadowing on lower dimensions, etc. more approaches to bring it to lower dimensions with or without transformation like basis transformation. In this situation, you should also feed data after this transformation for testing / using the model.
  • There are problems beyond model selection and preprocessing, feature engineering like data quality, overfitting, evaluation, errors, hyper parameter tuning and generalization which we need to think about.
  • Situations like classification of medical diagnosis issues have very high value repercussions for wrong classification whereas some models with overfitting won’t generalize and cause problems like in normal situations with regression.
  • Probability concepts like equally likely for randomness, distributions, fairness, probability tree, joint probability, permutations and combinations, etc. have lot of value and are worth learning.
  • There is no learning like learning by experience, concepts and application.
  • Your assumptions maybe wrong as well so it’s good to verify.
  • Accuracy alone may not be a good measure, please add recall & precision as well in your analysis especially in imbalanced datasets.
  • Domain knowledge matters. Don’t ignore this.
  • Website: www.HarwaniSystems.in
  • Blog: www.TechAndTrain.com/blog
  • LinkedIn: Neil Harwani | LinkedIn
  • Email: Neil@HarwaniSystems.in