Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.

1. Image Enhancement:

o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.

o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.

o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.

o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.

2. Image Restoration:

o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.

o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.

o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.

o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.

Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.

Let’s review these in detail:

1. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.

2. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.

3. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.

4. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).

5. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.

6. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.

7. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.

8. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.

As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,

• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.

• Key points from this research:

o Objective: To improve object detection accuracy and energy utilization.

o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.

o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.

o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.

o Energy Savings: Energy savings were achieved based on ambient lighting conditions.

• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.

Another topic involves improving energy efficiency of indoor lighting:

• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.

• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.

• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.

The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.

Previous articles on data processing: DM.docx  Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.

1. Image Enhancement:

o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.

o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.

o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.

o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.

2. Image Restoration:

o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.

o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.

o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.

o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.

Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.

Let’s review these in detail:

1. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.

2. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.

3. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.

4. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).

5. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.

6. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.

7. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.

8. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.

As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,

• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.

• Key points from this research:

o Objective: To improve object detection accuracy and energy utilization.

o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.

o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.

o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.

o Energy Savings: Energy savings were achieved based on ambient lighting conditions.

• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.

Another topic involves improving energy efficiency of indoor lighting:

• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.

• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.

• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.

The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.

Previous articles on data processing: DM.docx Image processing is a field of study that involves analyzing, manipulating, and enhancing digital images using various algorithms and techniques. These techniques can be broadly categorized into two main categories: image enhancement and image restoration.

3. Image Enhancement:

o Contrast Adjustment: Techniques like histogram equalization, contrast stretching, and gamma correction are used to enhance the dynamic range of an image.

o Filtering: Filtering techniques such as linear filters (e.g., mean, median, and Gaussian filters) and non-linear filters (e.g., edge-preserving filters) can be applied to suppress noise and enhance image details.

o Sharpening: Techniques like unsharp masking and high-pass filtering can enhance the sharpness and details of an image.

o Color Correction: Methods like color balance, color transfer, and color grading can adjust the color appearance of an image.

4. Image Restoration:

o Denoising: Various denoising algorithms, such as median filtering, wavelet-based methods, and total variation denoising, can be used to remove noise from images.

o Deblurring: Techniques like blind deconvolution and Wiener deconvolution are used to recover the original image from blurred versions.

o Super-resolution: Super-resolution techniques aim to enhance the resolution and details of low-resolution images by utilizing information from multiple images or prior knowledge about the image degradation process.

o Image Inpainting: Inpainting algorithms fill in missing or corrupted regions in an image by estimating the content from the surrounding areas.

Apart from these, there are several other advanced image processing techniques, such as image segmentation, object recognition, image registration, and feature extraction, which are widely used in fields like computer vision, medical imaging, and remote sensing.

Let’s review these in detail:

9. Image Filtering: This algorithm involves modifying the pixel values of an image based on a specific filter or kernel. Filters like Gaussian, median, and Sobel are used for tasks like smoothing, noise reduction, and edge detection.

10. Histogram Equalization: It is a technique used to enhance the contrast of an image by redistributing the pixel intensities. This algorithm is often used to improve the visibility of details in an image.

11. Image Segmentation: This algorithm partitions an image into multiple regions or segments based on specific criteria such as color, texture, or intensity. Segmentation is useful for tasks like object recognition, image understanding, and computer vision applications.

12. Edge Detection: Edge detection algorithms identify and highlight the boundaries between different regions in an image. Commonly used edge detection algorithms include Sobel, Canny, and Laplacian of Gaussian (LoG).

13. Image Compression: Image compression algorithms reduce the file size of an image by removing redundant or irrelevant information. Popular compression algorithms include JPEG, PNG, and GIF.

14. Morphological Operations: These algorithms are used for processing binary or grayscale images, mainly focusing on shape analysis and image enhancement. Operations such as dilation, erosion, opening, and closing are commonly used.

15. Feature Extraction: Feature extraction algorithms extract meaningful information or features from an image, which can be used for tasks like object recognition, pattern matching, and image classification. Techniques like Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) are commonly used.

16. Neural Networks: Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are widely used for image processing tasks. CNNs can automatically learn and extract features from images, making them highly effective for tasks like object detection, image classification, and image generation.

As with most algorithms, the quality of data plays an immense role in the output of the image processing. Image capture, continuous capture, lighting and best match among captures are some of the factors when comparing choices for the same image processing task. The use of lighting for better results in high contrast images is a significant area of research. For example,

• Recently, an embedded system was proposed that leverages image processing techniques for intelligent ambient lighting. The focus is on reference-color-based illumination for object detection and positioning within robotic handling scenarios.

• Key points from this research:

o Objective: To improve object detection accuracy and energy utilization.

o Methodology: The system uses LED-based lighting controlled via pulse-width modulation (PWM). Instead of external sensors, it calibrates lighting based on predetermined red, green, blue, and yellow (RGBY) reference objects.

o Color Choice: Yellow was identified as the optimal color for minimal illumination while achieving successful object detection.

o Illuminance Level: Object detection was demonstrated at an illuminance level of approximately 50 lx.

o Energy Savings: Energy savings were achieved based on ambient lighting conditions.

• This study highlights the importance of color choice and intelligent lighting systems in computer vision applications.

Another topic involves improving energy efficiency of indoor lighting:

• This proposes an intelligent lighting control system based on computer vision. It aims to reduce energy consumption and initial installation costs.

• The system utilizes real-time video stream data from existing building surveillance systems instead of traditional sensors for perception.

• By dynamically adjusting lighting based on visual cues, energy efficiency can be improved.

The book "Active Lighting and Its Application for Computer Vision" covers various active lighting techniques. Photometric stereo and structured light are some examples. Actively controlling lighting conditions helps to enhance the quality of captured images and improve subsequent processing.

Previous articles on data processing: DM.docx 

 

Subarray Sum equals K 

Given an array of integers nums and an integer k, return the total number of subarrays whose sum equals to k. 

A subarray is a contiguous non-empty sequence of elements within an array. 

Example 1: 

Input: nums = [1,1,1], k = 2 

Output: 2 

Example 2: 

Input: nums = [1,2,3], k = 3 

Output: 2 

Constraints: 

• 1 <= nums.length <= 2 * 104 

• -1000 <= nums[i] <= 1000 

• -107 <= k <= 107 

 

class Solution { 

    public int subarraySum(int[] nums, int k) { 

        if (nums == null || nums.length == 0) return -1; 

        int[] sums = new int[nums.length];    

        int sum = 0; 

        for (int i = 0; i < nums.length; i++){ 

            sum += nums[i]; 

            sums[i] = sum; 

        } 

        int count = 0; 

        for (int i = 0; i < nums.length; i++) { 

            for (int j = i; j < nums.length; j++) { 

                int current = nums[i] + (sums[j] - sums[i]); 

                if (current == k){ 

                    count += 1; 

                } 

            } 

        } 

        return count; 

    } 

} 

 

[1,3], k=1 => 1 

[1,3], k=3 => 1 

[1,3], k=4 => 1 

[2,2], k=4 => 1 

[2,2], k=2 => 2 

[2,0,2], k=2 => 4 

[0,0,1], k=1=> 3 

[0,1,0], k=1=> 2 

[0,1,1], k=1=> 3 

[1,0,0], k=1=> 3 

[1,0,1], k=1=> 4 

[1,1,0], k=1=> 2 

[1,1,1], k=1=> 3 

[-1,0,1], k=0 => 2 

[-1,1,0], k=0 => 3 

[1,0,-1], k=0 => 2 

[1,-1,0], k=0 => 3 

[0,-1,1], k=0 => 3 

[0,1,-1], k=0 => 3 

 

 

 

 This is a summary of the book titled “The BRAVE Leader” written by David McQueen and published by Practical Inspirational Publishing in 2024. The author is a leadership coach who asserts that failing to model inclusivity has dire consequences for a leader no matter how busy they might get. They must empower all their people and create systems that serve a wide range of stakeholders’ needs. They can do so and more by following the Bold, Resilient, Agile, Visionary, and Ethical Leadership style.

The framework in this book tackles root causes and expands emerging possibilities. It helps to drive innovation while having a strategic thinking. Inclusive practices can be embed into the “DNA” of the organization. Honesty, transparency and a culture to drive antifragility  will help with a systemic change.

Good leaders inspire and empower others in various contexts, including community projects, sports games, and faith groups. Leadership is not just about management, but also involves understanding an organization's norms, values, and external factors. Leaders need followers to buy into their vision and actively participate in the work. Inclusive leadership involves attracting, empowering, and supporting talented individuals to achieve common goals without marginalizing them. To achieve this, leaders must be "BRAVE" - bold, resilient, agile, visionary, and ethical. This requires systems thinking and the ability to sense emerging possibilities. To be a BRAVE leader, leaders should focus on creating a culture where team members can develop their leadership qualities. They should resist the temptation to position themselves as an omnipotent "hero leader" and consider their decision-making approach. To align with the BRAVE framework, leaders should consider boldness, resilience, agility, vision, and ethicalness in their decision-making approaches.

BRAVE leaders use the "five W's" approach to problem-solving, which involves identifying the issue, identifying the business area, determining the deadline, identifying the most affected stakeholders, and determining the success of the problem. This approach helps in identifying the root cause of the problem and addressing it.

Strategic thinking is crucial for driving innovation and thriving amid uncertainty. It involves examining complex problems, identifying potential issues and opportunities, and crafting action plans for achieving big-picture goals. Inclusive leadership is essential for organizations to avoid homogeneous decision-making and foster a culture that combines inclusivity and strategic thinking.

Implementing inclusive practices into the organization's DNA includes rethinking recruitment, hiring practices, performance management, offboarding, and mapping customer segments. This involves rethinking the "best" applicants, updating hiring practices, and fostering a culture that combines inclusivity and strategic thinking. By embracing diversity and fostering a culture of inclusivity, organizations can thrive in the face of uncertainty and drive innovation.

Inclusive practices should be incorporated into an organization's DNA, including recruitment, performance management, offboarding, mapping customer segments, and product development. Expand the scope of applicants and provide inclusive hiring training to team members. Be more inclusive in performance management by asking questions and viewing performance reviews as opportunities for improvement. Treat exit interviews as learning experiences and consider customer needs and characteristics. Ensure stakeholders feel included in product development, ensuring they feel part of a two-way relationship.

Self-leadership is essential for effective leadership, as it involves understanding oneself, identifying desired experiences, and intentionally guiding oneself towards them. BRAVE leaders model excellence, embracing self-discipline, consistency, active listening, and impulse control. They prioritize their mental and physical health, taking breaks and vacations to show team members that self-care is crucial. Leadership coaching can help develop BRAVE characteristics and identify interventions for long-term changes.

Inclusive leadership requires a positive organizational climate, where employees feel valued, respected, and included. Building a BRAVE organizational culture involves setting quantifiable goals and holding managers and leaders accountable for meeting them. Diverse teams benefit from a broader range of insights, perspectives, and talents, and problem-solving more effectively by approaching challenges from multiple angles.

By embracing courage and overcoming fear, leaders drive systemic change, leading to courageous decision-making, better management, and a systematic approach to leadership. By cultivating positive characteristics like generosity, transparency, and accountability, leaders can drive sustainable growth and foster a more inclusive environment.

Previous Book Summaries: BookSummary86.docx 

Summarizing Software: SummarizerCodeSnippets.docx: https://1drv.ms/w/s!Ashlm-Nw-wnWhOYMyD1A8aq_fBqraA?e=BGTkR7

 Data mining algorithms are powerful tools used in various fields to analyze and extract valuable insights from large datasets. These algorithms are designed to automatically discover patterns, relationships, and trends in data, enabling organizations and researchers to make informed decisions.

Here are some commonly used data mining algorithms:

1. Decision Trees: Decision trees are tree-like structures that represent decisions and their possible consequences. They are used to classify data based on a set of rules derived from the features of the dataset.

2. Random Forests: Random forests are an ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting. Each tree in the forest is trained on a random subset of the data.

3. Naive Bayes: Naive Bayes is a probabilistic classifier based on Bayes' theorem. It assumes that the features are independent of each other, which simplifies the calculations. Naive Bayes is commonly used for text classification and spam filtering.

4. Support Vector Machines (SVM): SVM is a supervised learning model used for classification and regression analysis. It separates data points into different classes by finding an optimal hyperplane that maximizes the margin between the classes.

5. K-means Clustering: K-means is an unsupervised learning algorithm used for clustering analysis. It partitions data into K clusters based on their similarity, where K is a predefined number. It aims to minimize the intra-cluster variance and maximize the inter-cluster variance.

6. Neural Networks: Neural networks are artificial intelligence models inspired by the human brain's structure and function. They consist of interconnected nodes (neurons) organized in layers. Neural networks can be trained to recognize patterns, make predictions, and classify data.

7. Deep Learning: Deep learning is a subset of neural networks that involves training models with multiple layers. It has achieved significant breakthroughs in image recognition, natural language processing, and other complex tasks.

8. Association Rule Mining: Association rule mining is used to discover relationships and dependencies between items in a dataset. It identifies frequent itemsets and generates rules based on their co-occurrence.

9. Reinforcement Learning: Reinforcement learning is an AI technique where an agent learns to make optimal decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, which guide its learning process.

10. Genetic Algorithms: Genetic algorithms are optimization techniques inspired by the process of natural selection. They use principles of genetics and evolution to iteratively search for the best solution in a large solution space.

These algorithms are just a small sample of the vast array of techniques available in data mining and artificial intelligence. Each algorithm has its strengths and weaknesses, and the choice depends on the specific problem and dataset at hand.

Reference: https://1drv.ms/w/s!Ashlm-Nw-wnWxBFlhCtfFkoVDRDa?e=aVT37e  

 This is a summary of the book titled “Reputation Analytics: Public opinion for companies” written by Daniel Diermeier and published by University of Chicago Press in 2023. This book outlines the necessity and method for a corporation to protect itself from a corporate reputation crisis. The author explains how small actions and even inactions can cascade into a massive crisis and potentially harm the business, even in the long run. By providing examples and learnings, the author provides a step-by-step framework to achieve that goal. Some of the highlights are that managing a corporate reputation is like thinking as a political strategist. People form both specific and general impressions and they do so in six primary ways. Companies face reputational crises when they trigger a “moral outrage”. It is difficult to fight perceptions that a brand is causing harm, so taking accountability becomes a consideration. The tasks in an activist’s campaign are something that a company must be comfortable managing. Leveraging a deep understanding of media and social network influence and harnessing emerging technologies are necessary. A risk management mindset that avoids common mistakes also helps.

Managing a corporate reputation is similar to managing public opinion, but companies must consider various publics, including customers, employees, investors, business partners, suppliers, and external groups like regulators and the media. Successful reputation management requires assuming external actors' perspectives and viewpoints, as public perceptions are not always rooted in direct experiences and may differ across constituencies, products, and markets. People form specific and general impressions of a brand in six primary ways: repetition, relevance, attention, affect, concordance, and online processing. Companies face reputational crises when they trigger "moral outrage," which is emotional response to a brand's break with ethical norms or values. Moral judgment hinges on three main principles: the duty to avoid causing others harm, upholding fairness, justice, and rights, and respecting moral conventions and values. People employ two modes of thinking when making moral judgments: experiential (experiential) and analytical (logic-based). Companies must make reputation management an integral part of their strategic operations to avoid reputational crises and maintain a positive brand image.

Brands must take accountability for their actions and consider "folk economics" before taking action. The public's perception of commerce and industry can affect a company's reputation. Companies should fight against accusations with clear, easy-to-understand arguments and apologize for any harm caused. Leaders should demonstrate commitment to handling crises and empathy towards those harmed by the company's actions. Modern companies are more likely to face activist campaigns that damage reputations due to increased ethical expectations, media criticism, and trust-based business models. Social activism is more common and less localized, thanks to social media. Companies should adopt corporate social responsibility (CSR) practices but not be afraid of activist attacks. Statistical modeling should consider these factors to avoid misinterpretation. Companies should also leverage a deep understanding of media and social network influence to avoid negative media coverage that can trigger a reputational crisis. For example, Toyota's stock prices plummeted after a car crash, despite the company's overall safety record.

Perceptions and attitudes are influenced by peers, third-party experts, and media, both traditional and user-generated. Building and maintaining a successful reputation in the marketplace requires a deep understanding of these channels of influence. Media outlets can play a significant role in determining the issues to which people pay attention, and when one company in a particular industry or product area comes under media scrutiny, the potential for reputational damage increases for all businesses in that sector and those in closely related sectors. Social media also wields influence over public opinion, and using linear regression models can help identify triggers for a rise in certain variables.

To manage corporate reputation proactively, organizations should explore alternative ways of collecting and analyzing consumer data, such as sentiment analysis, machine learning algorithms, text-analytic scores, and supervised learning models. A risk management mindset is essential, as people will consider a company's current actions and past actions when under public scrutiny.

To avoid reputational crises, shift from reactive crisis management to proactive risk management. Develop a reputation management system into your corporate strategy and appoint a tactical team to oversee it. Regularly update leadership on potential risks and employ preparation strategies for those you cannot avoid. Invest time in assessing important issues that could risk reputational damage. Monitor emerging issues and respond accordingly. By developing a proactive reputation management capability, you increase the likelihood of preventing crises before they occur.

Summarizing Software: 

SummarizerCodeSnippets.docx

##codingexercise https://1drv.ms/w/s!Ashlm-Nw-wnWhO1TAZ1Y860-W7-vGw?e=s3pvmb

 This is a summary of the book titled “Leveraged: the new economics of debt and financial fragility” written by Prof.Moritz Schularick and published by University of Chicago Press in 2022. This collection of essays presents an overview of the latest thinking and their practical implications. Assumptions such as those before 2008 that financial institutions will just be fine, are questioned in some contexts and the work of Hyman Minsky who explained human nature’s tendency towards boom-and-bust cycles is a recurring theme and inspiration.

Credit and leverage are fundamental factors in recent crises. Credit booms distort economies and slowdowns follow. A banking system with higher capital to lending ratios does not affect the financial crisis. Financial sector expectations drive lending booms and busts. When credit grows, the price of risk is lowered. A historical categorization of financial crises might just be worth it. This might reveal that a great depression might have been a credit boom gone wrong. Even though credit plays such a big role in creating instability, its policy implications are far from straightforward.

Credit booms distort economies and lead to economic slowdowns. Current financial system regulation is too focused on minimizing the risk of banks getting into trouble, which leads to a dramatic drop in consumer spending and loss of confidence in the wider economy. To address this, the current structure of banking regulation should split risk between creditors and debtors in a socially beneficial manner. One way to tackle this is using "state-contingent contracting" (SCCs), which automatically reduce the amount a borrower needs to pay back during a downturn. Examples of SCCs include student loans and loans to countries based on GDP growth. Credit booms often generate distortions and vulnerabilities that often end in crises. The 2008 financial crisis revealed that both executives and shareholders take risks underwritten by the taxpayer. To address this, "lockups" or "debt-based compensation" for bankers' pay could be created, setting the condition that there will not be bankruptcies or taxpayer bailouts for some time after the remuneration period.

Excessive subprime lending was a popular narrative that led to the 2008 US financial crisis. However, non-investors, such as real estate investors, often had other non-real estate loans in distress, leading to policy implications that differ from those based on the notion that subprime borrowers drove the crisis. Young professionals, who were approximately 14% of all borrowers, represented almost 50% of foreclosures during the crisis's peak. A banking system with higher capital-to-lending ratios does not affect the likelihood of a financial crisis. Despite regulations increasing capital after previous crises, no evidence suggests that banks with more capital suffered less during that period. Research shows that better capital ratios do have an influence on recovery from a crisis. Financial sector expectations drive lending booms and busts, as they amplify trends of the recent past and neglect the mean reversion that long-term data suggests.

Investment industry methodology could improve the process of assessing the riskiness of banks, as recent crises have shown. Portfolio-assessing methodology, which combines market data and bank accounting data, could be a useful tool for banks to assess their risk. Studies show that low asset volatility in the past can predict credit growth, as agents update their views on risk based on the past and are overoptimistic about risk going forward. This could lead to excessive risk, resulting in fragility and raising the likelihood of a bad event.

A comprehensive historical categorization of financial crises is valuable, as it focuses on real-time metrics like bank equity returns, credit spread measures, credit distress metrics, nonperforming loan rates, and other bank data. This quantitative approach contrasts with the vagaries of commentators reporting on financial crises and the filtration of narratives by historians.

Narrative accounts of crises are still valuable, but research reveals that some "quiet crises" with less impact on the general economy have been forgotten or misunderstood. The spread of government-backed deposit insurance and the shift from lending to businesses to real estate were significant events in the US Great Depression.

The US Great Depression may have been a credit boom gone wrong, as credit played a crucial role in generating the bubble. The growth of the money supply continued until 1926, but credit growth continued for a few manic years. Total private credit reached 156% in 1929, more than other developed countries. The New York Fed pressured member banks to cap brokers' loans, but interest rates on brokers' loans proved attractive, leading to nonmember banks, financial institutions, companies, and individuals filling the gap. The Federal Reserve raised interest rates in 1928 to contain the boom, but the stock market continued to rise, attracting money from abroad. The importance of credit in creating financial instability has revived since the 2008 crisis. Evidence suggests that the allocation of credit matters as much as its quantity, and excessive credit directed toward real estate is more likely to come before a financial crisis.

 This is a summary of the book titled “Leveraged: the new economics of debt and financial fragility” written by Prof.Moritz Schularick and published by University of Chicago Press in 2022. This collection of essays presents an overview of the latest thinking and their practical implications. Assumptions such as those before 2008 that financial institutions will just be fine, are questioned in some contexts and the work of Hyman Minsky who explained human nature’s tendency towards boom-and-bust cycles is a recurring theme and inspiration.

Credit and leverage are fundamental factors in recent crises. Credit booms distort economies and slowdowns follow. A banking system with higher capital to lending ratios does not affect the financial crisis. Financial sector expectations drive lending booms and busts. When credit grows, the price of risk is lowered. A historical categorization of financial crises might just be worth it. This might reveal that a great depression might have been a credit boom gone wrong. Even though credit plays such a big role in creating instability, its policy implications are far from straightforward.

Credit booms distort economies and lead to economic slowdowns. Current financial system regulation is too focused on minimizing the risk of banks getting into trouble, which leads to a dramatic drop in consumer spending and loss of confidence in the wider economy. To address this, the current structure of banking regulation should split risk between creditors and debtors in a socially beneficial manner. One way to tackle this is using "state-contingent contracting" (SCCs), which automatically reduce the amount a borrower needs to pay back during a downturn. Examples of SCCs include student loans and loans to countries based on GDP growth. Credit booms often generate distortions and vulnerabilities that often end in crises. The 2008 financial crisis revealed that both executives and shareholders take risks underwritten by the taxpayer. To address this, "lockups" or "debt-based compensation" for bankers' pay could be created, setting the condition that there will not be bankruptcies or taxpayer bailouts for some time after the remuneration period.

Excessive subprime lending was a popular narrative that led to the 2008 US financial crisis. However, non-investors, such as real estate investors, often had other non-real estate loans in distress, leading to policy implications that differ from those based on the notion that subprime borrowers drove the crisis. Young professionals, who were approximately 14% of all borrowers, represented almost 50% of foreclosures during the crisis's peak. A banking system with higher capital-to-lending ratios does not affect the likelihood of a financial crisis. Despite regulations increasing capital after previous crises, no evidence suggests that banks with more capital suffered less during that period. Research shows that better capital ratios do have an influence on recovery from a crisis. Financial sector expectations drive lending booms and busts, as they amplify trends of the recent past and neglect the mean reversion that long-term data suggests.

Investment industry methodology could improve the process of assessing the riskiness of banks, as recent crises have shown. Portfolio-assessing methodology, which combines market data and bank accounting data, could be a useful tool for banks to assess their risk. Studies show that low asset volatility in the past can predict credit growth, as agents update their views on risk based on the past and are overoptimistic about risk going forward. This could lead to excessive risk, resulting in fragility and raising the likelihood of a bad event.

A comprehensive historical categorization of financial crises is valuable, as it focuses on real-time metrics like bank equity returns, credit spread measures, credit distress metrics, nonperforming loan rates, and other bank data. This quantitative approach contrasts with the vagaries of commentators reporting on financial crises and the filtration of narratives by historians.

Narrative accounts of crises are still valuable, but research reveals that some "quiet crises" with less impact on the general economy have been forgotten or misunderstood. The spread of government-backed deposit insurance and the shift from lending to businesses to real estate were significant events in the US Great Depression.

The US Great Depression may have been a credit boom gone wrong, as credit played a crucial role in generating the bubble. The growth of the money supply continued until 1926, but credit growth continued for a few manic years. Total private credit reached 156% in 1929, more than other developed countries. The New York Fed pressured member banks to cap brokers' loans, but interest rates on brokers' loans proved attractive, leading to nonmember banks, financial institutions, companies, and individuals filling the gap. The Federal Reserve raised interest rates in 1928 to contain the boom, but the stock market continued to rise, attracting money from abroad. The importance of credit in creating financial instability has revived since the 2008 crisis. Evidence suggests that the allocation of credit matters as much as its quantity, and excessive credit directed toward real estate is more likely to come before a financial crisis.

 This is a continuation of a previous article on cloud resources, their IaC, shortcomings and resolutions with some more exciting challenges to talk about. The previous article cited challenges and resolutions with regards to Azure Front Door and its backend services aka origins. This article focuses on ip access restrictions of the origins such as app services but we resume from the earlier mentioned best practices that a good access restriction will not only specify the ip address range of the sender but also verify the header which in the case of Azure Front Door is x-Azure-FDID and is stamped by the Front Door with its GUID. Since the GUID is specific to the instance of the typically unique and global Front Door in most deployments, a rule that checks the header only needs one value to compare against.  This header is set by the Front Door on every request so the access restriction rule works against every request.

In this case, the app services must be configured to do IP address filtering to accept traffic from the Front Door’s backend IP address space and Azure’s infrastructure only. As pointed out earlier, this does not mean the ip addresses to which the Front Door’s endpoint resolves to. Instead a complete list of Ip addresses for the backend can be found with the use of a service tag named AzureFrontDoor.Backend which comes helpful not only to find the ip addresses but also to configure rules in the network security group, if desired. The backend ip addresses can be found from their publication at https://www.microsoft.com/download/details.aspx?id=56519 and appropriate CIDR ranges can be determined to encompass all. Note that these pertain to a large number of locations, specifically metros that are spread the world over.  Should an ipv6 CIDR be need for these ip ranges, they can be succinctly denoted by 2a01:111:2050::/44 range.

On the other hand, traffic from the Azure’s basic infrastructure services will originate from the virtualized host ip addresses of 168.63.129.16 through 169.254.169.254.