This document provides an overview of neural networks and their potential applications in accounting and auditing. It discusses how neural networks work, their history of use since the 1990s, and current applications in areas like continuous auditing, fraud detection, and improving auditor decisions. While neural networks have seen limited adoption in accounting and auditing so far, the document argues they could benefit the field by identifying patterns in large datasets that humans may miss. It recommends auditing professionals implement neural network models with a full-time commitment to help direct their work.
In this research brief, we talk about the role of machine learning and artificial intelligence in Observability. The market is still in early stages and we expect mainstream adoption in the next 2-3 years. It is time for Modern IT Operations/SRE/DevOps teams to understand how Observability is different from traditional marketing and how it can help run resilient services with cloud native architectures.
Ediscovery tools underutilized and misunderstood in Hong Kong Kate Chan
According to a recent survey Kroll Ontrack conducted in Hong Kong, companies here have a limited understanding of how to effectively utilize ediscovery tools, and companies are deterred by the fact that they still don’t truly understand what it is.
The Future of Artificial Intelligence and Quality Management in Hospitals By....Healthcare consultant
The medical device industry has noticed this factor and uses it to save lives. Artificial intelligence (AI) in the life sciences industry is capable of more than one could imagine and it’s changing the future. For example, one organization is creating AI-based voice robot technology, which, according to an article in Management Matters Network, will deliver custom prescriptive advice to managers using strengths and performance data to help better coach and engage employees.
Automated audit management has served as a great source of information to delve deeper into data with predictive intelligence regarding safety and compliance. Leading safety metrics provide:
• Total number of noncompliances
• Number of near-misses enabling investigation to prevent potential incidents
• The time it takes to complete post-audit corrective and preventive actions
• Easy-to-view previous findings for corrective action launches and findings
• Automated audit management software that centralizes all risk items and allows users to automatically assess them and generate reports quickly to pinpoint high-risk gaps that may otherwise go unnoticed
The following article describes a new technology based on a distributed system such as Blockchain and powered by machine learning algorithms. The NeuroChain technology is a fusion between Blockchain and machine Learning, and based on three pillars:
- A decision maker : A Chain of Bots
- A set of rules : the Decision Protocol (Proof of Involvement and Integrity & Proof of Workflow)
- A network and media : the Pragmatic Communication Channels (adaptive communication protocol) and Learning ecosystem.
In this research brief, we talk about the role of machine learning and artificial intelligence in Observability. The market is still in early stages and we expect mainstream adoption in the next 2-3 years. It is time for Modern IT Operations/SRE/DevOps teams to understand how Observability is different from traditional marketing and how it can help run resilient services with cloud native architectures.
Ediscovery tools underutilized and misunderstood in Hong Kong Kate Chan
According to a recent survey Kroll Ontrack conducted in Hong Kong, companies here have a limited understanding of how to effectively utilize ediscovery tools, and companies are deterred by the fact that they still don’t truly understand what it is.
The Future of Artificial Intelligence and Quality Management in Hospitals By....Healthcare consultant
The medical device industry has noticed this factor and uses it to save lives. Artificial intelligence (AI) in the life sciences industry is capable of more than one could imagine and it’s changing the future. For example, one organization is creating AI-based voice robot technology, which, according to an article in Management Matters Network, will deliver custom prescriptive advice to managers using strengths and performance data to help better coach and engage employees.
Automated audit management has served as a great source of information to delve deeper into data with predictive intelligence regarding safety and compliance. Leading safety metrics provide:
• Total number of noncompliances
• Number of near-misses enabling investigation to prevent potential incidents
• The time it takes to complete post-audit corrective and preventive actions
• Easy-to-view previous findings for corrective action launches and findings
• Automated audit management software that centralizes all risk items and allows users to automatically assess them and generate reports quickly to pinpoint high-risk gaps that may otherwise go unnoticed
The following article describes a new technology based on a distributed system such as Blockchain and powered by machine learning algorithms. The NeuroChain technology is a fusion between Blockchain and machine Learning, and based on three pillars:
- A decision maker : A Chain of Bots
- A set of rules : the Decision Protocol (Proof of Involvement and Integrity & Proof of Workflow)
- A network and media : the Pragmatic Communication Channels (adaptive communication protocol) and Learning ecosystem.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
How to Use Open Source Technologies in Safety-critical Digital Health Applica...Shahid Shah
Presented at 3rd Annual Open Source EHR Summit - Key Takeaways:
* Outcomes driven care (vs. fees for service or volume driven care) is in our future
* Because outcomes now matter more than ever, open source digital health solutions are even more important
* There are new realities of patient populations driving open source even faster
* How to use open source reliably and and securely in a safety-critical environment like medical devices
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Architecting, designing and building medical devices in an outcomes focused B...Shahid Shah
Keeping your medical device designs relevant in an era of value based and outcome driven care is not easy. In this talk, I cover the following topics:
* “Connected EHRs”, device interoperability, and “Accountable Tech” are the future of med devices
* Hardware, sensors, and software are transient businesses but data lives forever. He who owns, integrates, and uses data wins in the end.
* Data from devices is too important and specialized to be left to software vendors, managed service providers, and system integrators.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
The Next Step For Aritificial Intelligence in Financial ServicesAccenture Insurance
As financial services firms strive to transform their businesses for a digital world, realize efficiencies, improve the customer experience and revitalize their growth, they increasingly see artificial intelligence-based (AI) technologies as key. For firms, the next wave of AI innovation are artificial neural networks.
Imperfect look at possible applications of Web Based Sentiment Engine MECB 2012.
Sentiment analysis involves classifying opinions from text as "positive", "negative" or “neutral. Its purpose and benefit is to assist in extracting valuable information and insight from copious amounts of unstructured data. This proposed system will have the capability to determine online sentiment on current affairs for the purpose of analysis and prediction. For the sentiment analysis a cluster-method approach is recommended, which is a recent advancement in this area. Various APIs will assist in extracting other data such as location and time. Evaluation of system through the use of the Pang et al movie review data sets is recommended to validate basic functionality and real life data in the form of the 2008 US presidential race data to evaluate all functionality of the system. Multiple industries are identified as potential users of this system from marketing companies to hotels adding to our benefit in the commercialisation potential of the system.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
Problems from the inside of an organization’s perimeters are a significant threat, since it is very difficult to
differentiate them from outside activity. In this dissertation, evaluate an insider threat detection motto on
its ability to detect different type of scenarios that have not previously been identify or contemplated by the
developers of the system. We show the ability to detect a large variety of insider threat scenario instances
We report results of an ensemble-based, unsupervised technique for detecting potential insider threat,
insider threat scenarios that robustly achieves results. We explore factors that contribute to the success of
the ensemble method, such as the number and variety of unsupervised detectors and the use of existing
knowledge encoded in scenario based detectors made for different known activity patterns. We report
results over the entire period of the ensemble approach and of ablation experiments that remove the
scenario-based detectors.
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
Phishing Websites Detection Using Back Propagation Algorithm: A Reviewtheijes
Phishing is an illicit modus operandi employing both societal engineering and technological subterfuge to theft client’s private identity data and monetary account credentials. Influence of phishing is pretty radical as it engrosses the menace of identity larceny and financial losses. This paper elucidates the back propagation paradigm to instruct the neural network for phishing forecast. We execute the root-cause analysis of phishing and incentive for phishing. This analysis is intended at serving developers the effectiveness of neural networks in data mining and provides the grounds proving neural networks in phishing detection.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
How to Use Open Source Technologies in Safety-critical Digital Health Applica...Shahid Shah
Presented at 3rd Annual Open Source EHR Summit - Key Takeaways:
* Outcomes driven care (vs. fees for service or volume driven care) is in our future
* Because outcomes now matter more than ever, open source digital health solutions are even more important
* There are new realities of patient populations driving open source even faster
* How to use open source reliably and and securely in a safety-critical environment like medical devices
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Architecting, designing and building medical devices in an outcomes focused B...Shahid Shah
Keeping your medical device designs relevant in an era of value based and outcome driven care is not easy. In this talk, I cover the following topics:
* “Connected EHRs”, device interoperability, and “Accountable Tech” are the future of med devices
* Hardware, sensors, and software are transient businesses but data lives forever. He who owns, integrates, and uses data wins in the end.
* Data from devices is too important and specialized to be left to software vendors, managed service providers, and system integrators.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
The Next Step For Aritificial Intelligence in Financial ServicesAccenture Insurance
As financial services firms strive to transform their businesses for a digital world, realize efficiencies, improve the customer experience and revitalize their growth, they increasingly see artificial intelligence-based (AI) technologies as key. For firms, the next wave of AI innovation are artificial neural networks.
Imperfect look at possible applications of Web Based Sentiment Engine MECB 2012.
Sentiment analysis involves classifying opinions from text as "positive", "negative" or “neutral. Its purpose and benefit is to assist in extracting valuable information and insight from copious amounts of unstructured data. This proposed system will have the capability to determine online sentiment on current affairs for the purpose of analysis and prediction. For the sentiment analysis a cluster-method approach is recommended, which is a recent advancement in this area. Various APIs will assist in extracting other data such as location and time. Evaluation of system through the use of the Pang et al movie review data sets is recommended to validate basic functionality and real life data in the form of the 2008 US presidential race data to evaluate all functionality of the system. Multiple industries are identified as potential users of this system from marketing companies to hotels adding to our benefit in the commercialisation potential of the system.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
Problems from the inside of an organization’s perimeters are a significant threat, since it is very difficult to
differentiate them from outside activity. In this dissertation, evaluate an insider threat detection motto on
its ability to detect different type of scenarios that have not previously been identify or contemplated by the
developers of the system. We show the ability to detect a large variety of insider threat scenario instances
We report results of an ensemble-based, unsupervised technique for detecting potential insider threat,
insider threat scenarios that robustly achieves results. We explore factors that contribute to the success of
the ensemble method, such as the number and variety of unsupervised detectors and the use of existing
knowledge encoded in scenario based detectors made for different known activity patterns. We report
results over the entire period of the ensemble approach and of ablation experiments that remove the
scenario-based detectors.
A Model for Encryption of a Text Phrase using Genetic Algorithmijtsrd
"In any organization it is an essential task to protect the data from unauthorized users. Information Systems hardware, software, networks, and data resources need to be protected and secured to ensure quality, performance, and integrity. Security management deals with the accuracy, integrity, and safety of information resources. When effective security measures are in place, they can reduce errors, fraud, and losses. In the current work, the authors have proposed a model for encryption of a text phrase employing genetic algorithm. The entropy inherently available in genetic algorithm is exploited for introducing chaos in a text phrase thereby rendering it unreadable. The no of cross over points and mutation points decides the strength of the algorithm. The prototype of the model is implemented for testing the operational feasibility of the model and the few test cases are presented Dr. Poornima G. Naik | Mr. Pandurang M. More | Dr. Girish R. Naik ""A Model for Encryption of a Text Phrase using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23063.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-processing/23063/a-model-for-encryption-of-a-text-phrase-using-genetic-algorithm/dr-poornima-g-naik"
Phishing Websites Detection Using Back Propagation Algorithm: A Reviewtheijes
Phishing is an illicit modus operandi employing both societal engineering and technological subterfuge to theft client’s private identity data and monetary account credentials. Influence of phishing is pretty radical as it engrosses the menace of identity larceny and financial losses. This paper elucidates the back propagation paradigm to instruct the neural network for phishing forecast. We execute the root-cause analysis of phishing and incentive for phishing. This analysis is intended at serving developers the effectiveness of neural networks in data mining and provides the grounds proving neural networks in phishing detection.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
The Web and the Collective Intelligence - How to use Collective Intelligence ...Hélio Teixeira
The Web and the Collective intelligence - How to use Collective Intelligence techniques to ensure that your web application can extract valuable data from its usage and deliver that value right back to the users.
Web 2.0 Collective Intelligence - How to use collective intelligence techniqu...Paul Gilbreath
Source: http://www.helioteixeira.org/ How to use Collective Intelligence techniques to ensure that your web application can extract valuable data from its usage and deliver that value right back to the users. (MODULE 1)
Dynamic Rule Base Construction and Maintenance Scheme for Disease Predictionijsrd.com
Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
Similar to Neural networks in accounting and auditing slidecast (20)
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
2. Audio Synchronization Due to technical difficulties with Slideshare, it will be necessary to change slides manually, I apologize for this inconvenience. The slides are as follows:
3. Link to MP3 http://www.archive.org/details/NeuralNetworksSlidecastMp3
5. Introduction Neural Networks not widely used in audit Used mainly in science, and has some applications for fraud detection Has been tested and proposed for various uses in accounting and auditing
6. Background Neural networks are a type of artificial intelligence, and are based on the structure of the human brain and are composed of a large number of interconnected processors Pattern recognition is one of the most important aspects of the neural network technology The main advantage of artificial neural networks is that they can learn from their inputs and examples that are inputted into them can see relationships in data that will not be noticeable to human observers
8. History In 1994, neural networks were a new type of technology In 2003, large companies began to implement neural networks Robert Hecht-Nielsen, a professor at the University of California, San Diego, called neural networks “the most important scientific challenge of our time”
9. Current State of Neural Networks in Auditing Neural networks have been used in various applications across the scientific world, but they have not seen widespread implementation in the areas of accounting and auditing This section will examine the application of neural networks in various areas
10. Continuous Auditing Continuous auditing is defined as a type of auditing which produces audit results simultaneously, or a short period of time after, the occurrence of relevant events Internal auditors have a very prominent role in continuous auditing of a company Training efforts should have sufficient depth
11. Fraud Detection Neural networks have become the method of choice in the realm of fraud detection For example, a study used a neural network system to identify possible areas in financial data that would lead to fraud lawsuits The model will be able to assess this probability of fraud litigation
12. Accounting Buried deep within accounting data are patterns Accounts that traditionally are not viewed as having strong correlations with each other may in fact present substantial relationships
13. Auditor Decisions Many parts of the auditing field have been subject to neural network testing The two duties that stand out in terms of importance are the evaluation of a going concern opinion, and issuing qualified opinions in audit reports
14. Going Concern Being able to forecast earnings would allow for auditors to see if a company is likely to be able to continue as a going concern, and neural networks can establish patterns to see financial viability in the future No matter how many inputs are put into the system, and what the system generates as an output, it is still ultimately the auditor’s decision in the end
15. Qualified Opinions Being able to determine predictive patterns with regards to qualified audit opinions would allow auditors to “plan specific auditing procedures to achieve an acceptable level of audit risk” Large amounts of data could be used to focus auditors’ attention on possible problem areas that may result in a qualified opinion, and allow for a greater degree of testing and analysis with regards to these problem areas
16. Improvements? Their current lack of usage by the auditing profession should be reassessed The large amount of studies being performed over the past ten years is evidence of a growing number of academics and professionals who believe that this technology can provide great benefits to the auditing community
17. Cost Effective? It will become increasingly necessary to keep up by employing neural networks to observe patterns that are beyond the ability of human auditors Employing neural networks would also lead to a greater degree of accuracy in dangerous situations If auditors are not able to have a tool on their side that can generate predictive data about these issues, then they may not be able to keep pace with their own profession
18. Recommendations It is recommended that in the presence of evolving technology, a neural network model should be implemented into usage by auditing professionals Using the system would likely require a full-time commitment The neural network would have to be applied to each audit on an individual basis, so once a firm implements the necessary infrastructure for the network, individual audits can be analyzed
19. Conclusion Their specific application to various issues within the auditing world, such as continuous auditing, fraud detection, auditor decision making (going concern evaluation as well as issuing qualified audit opinions) could change the entire profession It is important for the auditing world to see that neural networks are not a replacement for the expertise and professional judgment of auditors, but simply a means of directing their attention and recognizing patterns in large amounts of data that humans would not see
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