The document describes an AI-driven Occupational Skills Generator (AIOSG) that aims to automate the process of creating occupational skills reference documents. The AIOSG utilizes an intelligent web crawler, natural language processing, neural networks, and a blockchain to gather data on occupational skills from various sources, analyze the data, and generate standardized skills reference documents. It is meant to make the document creation process more efficient, data-driven, and able to incorporate rapidly changing skills demands compared to the traditional manual process. The system architecture and key components of data collection, analysis, skills ontology construction, and reference document generation are outlined.
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
Open Source Platforms Integration for the Development of an Architecture of C...Eswar Publications
The goal of the Internet of Things (IoT) is to achieve the interconnection and interaction of all kind of everyday
objects. IoT architecture can be implemented in various ways. This paper presents a way to mount an IoT architecture using open source hardware and software platforms and shows that this is a viable option to collect information through various sensors and present it through a web page.
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability ...IJECEIAES
Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
Open Source Platforms Integration for the Development of an Architecture of C...Eswar Publications
The goal of the Internet of Things (IoT) is to achieve the interconnection and interaction of all kind of everyday
objects. IoT architecture can be implemented in various ways. This paper presents a way to mount an IoT architecture using open source hardware and software platforms and shows that this is a viable option to collect information through various sensors and present it through a web page.
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
International Journal of Computer Science, Engineering and Information Techn...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Computer Science, Engineering and Information Technology.
The Journal looks for significant contributions to all major fields of the Computer Science and
Information Technology in theoretical and practical aspects. The aim of the Journal is to provide
a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field.
All submissions must describe original research, not published or currently under review for another
conference or journal.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
How Can AI and IoT Power the Chemical Industry?Xiaonan Wang
AI, IoT and Blockchain tech briefing to the industry to showcase our research at NUS.
by Dr. Xiaonan Wang
Assistant Professor
NUS Department of Chemical & Biomolecular Engineering
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
International Journal of Computer Science, Engineering and Information Techn...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Computer Science, Engineering and Information Technology.
The Journal looks for significant contributions to all major fields of the Computer Science and
Information Technology in theoretical and practical aspects. The aim of the Journal is to provide
a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field.
All submissions must describe original research, not published or currently under review for another
conference or journal.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe.
Hosted by:
Sanjeev Sinha, President, CRISIL Global Research & Analytics
Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
How Can AI and IoT Power the Chemical Industry?Xiaonan Wang
AI, IoT and Blockchain tech briefing to the industry to showcase our research at NUS.
by Dr. Xiaonan Wang
Assistant Professor
NUS Department of Chemical & Biomolecular Engineering
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.
4 th International Conference on Data Science and Machine Learning (DSML 2023)gerogepatton
4
th International Conference on Data Science and Machine Learning (DSML 2023) will
act as a major forum for the presentation of innovative ideas, approaches, developments, and
research projects in the areas of Data Science and Machine Learning. It will also serve to
facilitate the exchange of information between researchers and industry professionals to
discuss the latest issues and advancement in the area of Data Science and Machine Learning.
Authors are solicited to contribute to the Conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the Computer Networks & Communications.
4th International Conference on Data Science and Machine Learning (DSML 2023) gerogepatton
4th International Conference on Data Science and Machine Learning (DSML 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Data Science and Machine Learning.
Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Machine learning applications used in accounting and auditsvivatechijri
AI is a territory of software engineering that gains from a lot of information, recognizes examples, and makes expectations about future occasions. In the accounting and auditing professions, Machine Learning has been progressively utilized over the most recent couple of years. Thusly, this investigation means to Survey the current Machine Learning applications in accounting and auditing with a focus on Big Four Organizations. In this study, the AI devices and stages created by Big Four organizations are analyzed by directing a content investigation. It has been distinguished that Big Four organizations built up a few Machine learning devices that are utilized for predictable audits coordination and the management, completely automated audits. Accounting processes such as accounts receivable and accounts payable management, preparation of expense reports, and risk assessment can easily be automated by AI. For instance, machine learning algorithms can match an invoice received, decide the right business ledger for acknowledgment, and place it in a payment pool where a human specialist can inspect and submit the payment request to the payment queue.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
A Study on the Applications and Impact of Artificial Intelligence in E Commer...ijtsrd
Trends in computer science show that various aspects of Artificial Intelligence are emerging, and other trends show that these advances are being applied to create intelligent in formation systems. In recent days artificial intelligence is changing the ways in which computers are usable as problem solving tools. The talent of humans is thus smartly creating and operating tools are indeed a feature of human based brainpower. This technology is now adapted by various E Commerce websites in order to identify the customer preference, pervious purchases, frequent checks etc. Google and Microsoft are also investing in artificial intelligence through various forms in order to enhance better customer service. The main aim of the study is to analysis and explores the various applications and impact of artificial intelligence in E Commerce industry. This study analyses and concludes that by replacement of human expert with artificial intelligence systems in E Commerce industry can significantly speedup and cheapens the production or service process. Prof. Lakshmi Narayan. N | Naveena. N "A Study on the Applications and Impact of Artificial Intelligence in E-Commerce Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26374.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26374/a-study-on-the-applications-and-impact-of-artificial-intelligence-in-e-commerce-industry/prof-lakshmi-narayan-n
Brno-IESS 20240206 v10 service science ai.pptxISSIP
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
Presentation made for the event "Digital transformation in France and Germany: Consequences for industry, society & higher education" organized by the French-German University in cooperation with Institut Mines-Télécom https://www.dfh-ufa.org/fr/digital-transformation-in-france-and-germany/
World of Watson 2016 - Artificial Intelligence ResearchKeith Redman
Have you ever noticed that all the movies made about the topic of Artificial Intelligence portray the doom of human kind and the hero or heroine’s success at averting it? Hopefully we never truly get to that point. However, if the inner geek in you is interested in checking out what IBM research is working on today, check out these sessions.
Similar to Ai driven occupational skills generator (20)
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
1. E-PROCEEDING OF THE 8TH INTERNATIONAL
CONFERENCE ON SOCIAL SCIENCES RESEARCH 2019
E-PROCEEDING OF THE 8TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES RESEARCH (ICSSR 2019).
(e-ISBN 978-967-0792-36-1). 18-19 November 2019, Imperial Heritage Hotel, Melaka, Malaysia.
Organised by https://worldconferences.net Page 29
AI-DRIVEN OCCUPATIONAL SKILLS GENERATOR (AIOSG)
Goon Wooi Kin (wk.goon@mimos.my), Kee Kok Yew (ky.kee@mimos.my), Nazarudin Mashudi
(nazarudin.mashudi@mimos.my), Amru Yusrin Amruddin (yusrin@mimos.my),
Ganesha Muthkumaran (ganesha.muthukumaran@mimos.my)
Enterprise Government Solutions Lab, Big Data Analytics Lab,
Artificial Intelligence Lab & Blockchain Lab,
Corporate Technology Division, MIMOS Berhad
ABSTRACT
The revolution of artificial intelligence (AI) is reshaping the world as we know it in terms of job
inequality and automation. Jobs are changing where the focus is moving more towards skillset rather
than academic qualifications as systems become more intelligent. Therefore, the need for every country
to align the skills development of their workforce towards the progression of technology is of paramount
importance. In this study, the authors present a novel idea and practical methods to capture and process
knowledge and experience in skillset to generate occupational skillset guidelines. This platform, from
here onward referred to as AI-driven Occupational Skills Generator (AIOSG), Malaysia’s applied
research and development center, MIMOS Berhad. AIOSG captures information on occupational
structure, occupational area, competency levels, competency profile, competency based curriculum, and
guidelines for assessment and training to create an occupational skills reference document. This
information is captured from market analysis, human resource departments (Government and private),
industry experts, and Internet literature. At present, such a reference document is produced manually
through conducting workshops involving industry experts. Hence, the document may not include
sufficient inputs from all active practitioners of an occupation, and it is often produced with some level
of obsolescence in that it lags behind current technology and process by the time it is published. AIOSG
leverages on AI’s strength in Natural Language Processing (NLP) and Neural Networks housed in a
web portal built on analytics to capture information from various contributing stakeholders on
occupational skills. The platform then processes relevant information and draws related information in
the creation of ontologies. All relevant information pertaining to a particular occupation is then
structured into a reference document which allows further review and inputs from experts. The platform
finally publishes the final, reviewed version of the document upon approval by the decision-making
authority and records of the data is stored on a blockchain. AIOSG cuts down the time and effort needed
while increasing the accuracy of information for a reference occupational skills document that training
institutes, employers, and employees potentially use to close the gap between the industry and skilled
workforce.
Field of Research: Artificial Intelligence, Occupational Skills, Skillset, Skills Development,
Competency, Training, Natural Language Processing, Neural Networks, Workforce, Human Resources,
Blockchain, Hyperledger, Web Crawler, Analytics, Labour Market
---------------------------------------------------------------------------------------------------------------------------
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Organised by https://worldconferences.net Page 30
1 Introduction
An occupational skills reference (OSR) document is a document that provides guidelines on the skill
sets necessary to perform tasks in a particular occupation. The document also provides a description of
the occupation, which is framed from the tasks and skill sets needed. Preparation of the OSR document
requires collection of and review of information with regard to the skillsets required to complete the
tasks under this occupation. The information includes, but is not limited to: interviews and workshops
with industry experts and review of labour market reports, projection reports of labour market demand
and supply, and other literature. With the current pace of advancement of technology and automation,
timeframe for skills relevance and demand for new and enhanced skills, the OSR for various
occupational sectors would need to be updated regularly.
However, the current process of production of an occupational skills reference (OSR) document is
resource-intensive, in terms of both time and cost. Lack of availability of individuals with sufficient
skillsets and well-developed industries with which to use and enhance those skills leads to less relevant
information on skills being captured and utilised for the creation of the OSR document. Conducting a
literature review of the data collected against existing market literature, reports and other pertinent
documents can be costly in both time and human resources. The limited ability of humans to forecast
the skills that may be in demand in the near future, is a shortcoming which may render the document
obsolete in a relatively short period after its publication. With these shortcomings in the current, human
labour-intensive process, process engineering and artificial intelligence would be employed to reduce
the time-to-market and financial cost of the OSR documents.
2 Methodology
This chapter first introduces the AIOSG then explains the system and components of AI-driven
Occupational Skills Generator.
The AIOSG is composed of three components:
1. Analytics in the form of an intelligent web crawler that crawls and analyses the web and specific
government/private databases (which includes information from market analysis, human
resource departments and industry experts) and literature repositories for:
a. information on the skills and competencies needed for a particular job title,
b. market demand for skillsets (existing, developing, non-existent in the current market),
and
c. regular feedback from industry experts on skills needed, both current and future, as
well as skills expected to become redundant.
2. An AI-based service that processes the information obtained from the crawler. The AIOSG
ontology construction comprises curriculum resource acquisition, domain concept extraction,
ontology relation mining, ontology description and ontology updating and leverages on Natural
Language Processing (NLP) and Neural Networks.
3. A blockchain backbone based on Hyperledger stores the records of the occupational skills and
to trace the changes and updates to the records through digital footprints as well as prevent the
records from being tampered.
All data is finally displayed on a visual dashboard for total bird’s eye view of the occupational skills
reference. This is used for decision making, publishing (generated into document format) and future
planning of the governing authority in collaboration with the industry lead bodies. Through this, it
ensures relevant and applicable occupational skills reference are applied and are able to be utilised for
and by the industry.
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(e-ISBN 978-967-0792-36-1). 18-19 November 2019, Imperial Heritage Hotel, Melaka, Malaysia.
Organised by https://worldconferences.net Page 31
The overall system for AIOSG is as follows:
Figure 1: AIOSG system architecture
3 AIOSG Analytics (Data Crawler & Analysis)
3.1 Topics Related to Skillset
Topics related to search term, in this case skillsets, contain semantic-related topics that can be used to
narrow down the search result by adding the topic to the initial search term. Figure 2 shows a screenshot
of topics related information in the Keyword Cloud.
Analytics
Layer
AIOSG UI
Presentation
Layer
Blockchain/
Data Layer Blockchain Distributed Records
Artificial
Intelligence
Layer
Integration
Layer Developer API
REST
Data Crawler
Filtering Engine
Relational
Engine
xxx
xxx
xxx
Language
Detector
ANN (SOM)
NLP
Text Extract
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Organised by https://worldconferences.net Page 32
Figure 2: Topics related to search term
3.2 Categories Related to Skillset
Categories related to search term contains semantic related categories that can be used to narrow down
the search result by adding the topic to the initial search term. Figure 3 shows a screenshot of categories
related information in the Keyword Cloud.
Figure 3: Other categories related to search term
3.3 Related Skillset Keyword Method
Figure 4 shows the process and flow to generating related keywords from user input search term.
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(e-ISBN 978-967-0792-36-1). 18-19 November 2019, Imperial Heritage Hotel, Melaka, Malaysia.
Organised by https://worldconferences.net Page 33
Figure 4: Process and flow to generating related keywords from user input search term
3.3.1 Language Detector
Figure 5 details the process of how these systems perform language detection on the search term:
User will insert search term in search form. Example: Computer
Language detector will detect insert search term languages status. Example: Computer
(English), Komputer (Malay).
Keyword is tagged with identified language.
Search term inserted
Search term tokenized
Each token compared with language service
(Example: Google language detection)
Language
detect service.
Each keyword tagged with its language
Figure 5: Language detector flow
Input:Search Term
Get relatedKeyword Search WWW
Comparecontentsof
search result with related
keywords
Assign frequency to
relatedkeywords
Output3: Topics
relatedto keyword
Output1: WWW Links (Web, Wiki,
News,Books, Blog, Location,
Video,Images)
Output2:
KeywordCloud
Ontology
Wiki
Lexical
Output4: Categories
relatedto keyword
LanguageDetector
KeywordGenerator
3.3.1
3.3.2
3.3.3
3.3.4
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3.3.2 Get Related Keyword
Each keyword then will go through the process as shown in Figure 6 to get any possible related keyword
from Wikipedia, Machine Readable Dictionary (MRD) or WordNet based on tagged language:
Token of keyword with tagged language
Each tokenized keyword will query wikipedia page
based on detected language. All hypertext linked word
will be grabbed and store to keywords repository
database.
Each tokenized keyword will query MRD thesaurus,
every synonym word in thesaurus will be stored to
keywords repository.
Each tokenized keyword will query WordNet, every
synonym word in WordNet will be stored to keywords
repository.
Keywords
repository
Figure 6: Get related keyword flow
3.3.3 Search Result Content Compared to Related Keywords
All search results on web content, news, blog, video description, image description from the Internet
search engine using the search term inserted by the user will be stored to temporary database. The
semantic similarity algorithm influenced by Noah et al. (2007) and Li et al. (2006) will aggregate all
stored search results using two-tier aggregation processes as shown in Figure 7:
a. Semantic similarity on WWW title:
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Search term WWW Titles
Joint word set
Raw semantic
vector 1
Raw semantic
vector 2
Semantic vector 1 Semantic vector 2
Semantic similarity
index
Wordnet
MRD
Figure 7: Semantic similarity algorithm
• Each web search title will be compared with search term insert by user using the method
proposed by the flow chart in Figure 7.
• Joint word sentence is:
S = S1 S2
= {w1, w2, ……., wn); wi are distinct
Example:
S1: software developer Malaysia (search term by user)
S2: ethical hacker (WWW search title)
S = {Malaysia, software, developer, ethical, hacker}.
S in distinct words generated from combination of S1 and S2.
• Each words in S1 and S2 will be compared with each words in S using
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8. E-PROCEEDING OF THE 8TH INTERNATIONAL
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Organised by https://worldconferences.net Page 36
• Each element in the matrix is compared with:
• where C is the set of unique overlap words found in the meanings of w1 and w2 and M refers
to the meanings of the respective words in WordNet. Therefore, r(C, Mw1) refers to the ratio
between the counts of meanings that contains any of the words in C with all the meaning
associated with w1.
• For the calculation of the semantic vector Si, the following formula is used:
The value of I(w) is calculated by referring to the MRD dictionary, using the following
formula:
• Then, the semantic similarity between the two compared sentences is simply the cosine
coefficient between the two semantic vectors.
• Ss (Semantic value of compared each search term and WWW title) will be stored in database.
Ss = 1.0 meaning search term is same with WWW title semantically else it gives 0 value.
b. Content categorisation:
• Each content of selected WWW title will be categorised using Naive Bayes (“Naïve Bayes
classifier,” n.d.) formula, the probability that a given document D contains all of the words
, given a class C is (Strickland, 2014, p. 75):
The source of set of words are using keywords repository by previous process as shown in
Figure 7.
3.3.4 Assign Frequency to Related Keywords
All top 10 WWW title with highest semantic similarity score and categorised score will be selected.
Figure 8 shows the process to assign frequency to related keywords:
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9. E-PROCEEDING OF THE 8TH INTERNATIONAL
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Organised by https://worldconferences.net Page 37
All words in top 10 selected WWW title content will be
tokenized
Each tokenized words will going thru to tag cloud
creation module
Higher frequency words will represent keywords with
big font in Keyword Cloud.
Finally related keywords is created.
Figure 8: Assign frequency to related keywords
Finally, related keywords from user input search term is created by following the method explained
above and they are listed in the Keyword Cloud section in the AIOSG system. The keywords in the
Keyword Cloud can either be used to initiate a new search or restart the search by adding the keyword
to the initial search term. This will return a narrowed down result, thus making the required information
more easily obtained.
4 AIOSG Artificial Intelligence
In the context of Artificial Intelligence (A.I.) using the ontology matching method is essential, this is
because the model can be grounded on element, structure, instance or multiple strategies (Hu et al.,
2008; Pirro and Talia, 2010; Belhadef, 2011; Liu et.al., 2012). As described in Zhu, Y. C., Zhang, W.,
He, Y., Wen, J.B., & Li, M. Y. (2018), multi strategy method works best because the conceptual
semantics and the hierarchy between concepts are weighted and integrated, while others lack either in
description function of attributes and relations or there is no intersection between the instance sets of
two ontologies.
As the AIOSG ontology is automated using web crawler, text mining and association rule mining,
Following this method, the ontology construction can be divided into five phases: curriculum resource
acquisition, domain concept extraction, ontology relation mining, ontology description and ontology
updating (Figure 9)(adapted from Zhu, Y. C., Zhang, W., He, Y., Wen, J.B., & Li, M. Y. (2018)).
10. E-PROCEEDING OF THE 8TH INTERNATIONAL
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Organised by https://worldconferences.net Page 38
Figure 9: Flow chart of the curriculum ontology construction
4.1 Natural Language Processing (NLP)
In order to upkeep the curriculum for variety of jobs and new skills in the market, the ontology extension
and update of the recent skills and job libraries has to be updated automatically, for which this area shall
require process matching ontology AI that works best. Among all the machine learning approach, there
are two applicable AI approach which are Natural Processing Language (NLP) and Neural Networks,
to be discussed in this paper.
Figure 10: Extracting Information from text and NLP Process
Figure 10 above shows how a web crawler extracts information from text, which also exhibits how
NLP and ontological processing works. Firstly, an ontology can be used directly when building the
lexicon, defining the terms (concepts and relations) for content words. Secondly, an ontology is a
knowledge base, expressed in a formal language, and therefore it provides knowledge for more complex
language processing.
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4.2 Neural Networks for Ontology Matching
In the framework for ontology matching, extended model of AIOSG represents the unsupervised neural
network based learning which is suitable to the knowledge structure because it fits the concepts and
relations for content words i.e. taxonomy. This is based on the applicability of a self-organising
map(SOM) or self –organising feature map(SOFM) as a type of artificial neural network(ANN) that is
trained to produce a low dimensional, discretised representation of the input space of the training
samples which is called a map, another method used in dimensionality reduction. The unique approach
in SOM as competitive learning is applied as opposed to error correction learning such as
backpropagation with gradient descent. Based on weight, the neurons are initialised either to small
random values or sampled evenly from the subspace spanned by the two largest principal component
eigenvectors. When a training example is used, its Euclidean distance to all weight vectors is computed,
of which the most similar weights to the inputs will be called best matching unit(BMU). The update
formula for a neuron v with weight vector Wᵥ(s) is
Where s is the step index, t an index into the training sample, u is the index of the BMU for the input
vector D(t), α(s) is a monotonically decreasing learning coefficient; θ(u,v,s) is the neighbourhood
function which gives the distance between the neuron u and the neuron v in steps.
Variables
These are the variables needed, with vectors in bold,
S is the current iteration
λ is the iteration limit
t is the index of the target input data vector in the input data set D
D(t) is a target input data vector
ʋ is the index of the best matching unit (BMU) in the map
θ( u, v, s) is a restraint due to distance from BMU, usually called the neighborhood function,
and
α(s) is a learning restraint due to iteration progress
Algorithm
1. Randomise the node weight vectors in a map
2. Randomly pick an input vector D(t)
3. Traverse each node in the map
Use the Euclidean distance formula to find the similarity between the input vector and
the map’s node’s weight vector
Track the node that produces the smallest distance (node is BMU)
4. Update the weight vectors of the nodes in the neighborhood of the BMU by pulling them closer
to the input vector
5. Increase s and repeat from step 2 while s< λ
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5 AIOSG Blockchain (Data Storage)
The data from the Artificial Intelligence block will then be stored in Blockchain, Hyperledger Fabric
(Hyperledger, 2019). It is the nature of blockchain itself where the data will be distributed among nodes
and immutable, which means, the data cannot be changed. This grabbing data from blockchain will
visualise a higher accuracy of outcome. The data entered has to be agreed in a consensus before storing
in the blockchain, which creates trusted data by the experts/AI. Even if a third party wants to change
data, the data will be changed in their own node. This action, however, will fail to add or modify the
information in the blockchain because the blockchain technology will always cross-refer with other
nodes if the data is the same and it will check if the block hash is the same as the previous block, thus
demonstrating immutability.
Figure 11: Representation of a blockchain network
Figure 12: Block containing transactions
Referring to Figure 12, blockchain creates blocks in an append-only structure. This disallows the ability
to delete and update. The data or transactions inside can be updated and will be added as a new block.
Each block has its own hash value before adding onto the chain of blocks.
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 8
Node 7
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Figure 13: Links of blocks
Referring to Figure 13, the data hash is linked to each block. This happens when a block is added upon
an agreed consensus. Due to this property of blockchain, if a person tried to edit their data, the block
will change and will not be linked to the previous hash. All nodes the nodes will then cross-refer each
other and check if the data is legitimate. If the data entered is not in concurrence with the data found in
the other nodes, the blockchain will pull the latest correct block from the other nodes.
5.1 Processed Data Stored in Blockchain
The processed skills and competencies data will be stored in the blockchain to ensure the integrity and
security of the original data. Figure 14 shows the process flow of how data is stored in the blockchain.
Figure 14: Data submission to blockchain flow
Processed data captured
using AI
Data is submited via an
invoke call to the
blockchain API
Transaction proposal
submited for consensus
approval
Acquire approval from
consensus and peers
Transaction signed by
peers and submitted for
block creation
Block containing
processsed skills and
competencies data is
created and appended to
the chain of blocks
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The processed skills and competencies data will be sent to blockchain API. This will then perform a
transaction proposal submission to the peers in the blockchain network. The peers will then endorse the
transaction proposal and return the simulated transactions and endorsing the peers’ signatures. The
application waits until it receives enough endorsed transaction proposals and will send the endorsed
transaction to the Ordering Peer Service to create a new block and update the ledger.
A smart contract or chaincode is required by the blockchain to store data inside. The following
chaincode shows how the occupation is added into the blockchain.
func (m *AIOSGChaincode) addOccupation(stub shim.ChaincodeStubInterface, args
[]string) pb.Response {
if len(args) != 7 {
return shim.Error("Incorrect number of arguements. Expecting 7 args")
}
// Input sanitation
fmt.Println("- Start Submit Occupation -")
if len(args[0]) <= 0 {// User
return shim.Error("User Required !")
}
if len(args[1]) <= 0 { // Project Name
return shim.Error("Occupation Required!")
}
user := args[0]
occp := args[1]
for tsid := 1; tsid >= 1; tsid++ {
tid := strconv.Itoa(tsid)
idAsBytes, err := stub.GetState(tid)
if err != nil {
return shim.Error("Failed to get state for " + tid)
}
if len(idAsBytes) == 0 {
fmt.Printf("No record found for " + tid + " ! Safe to add !")
occupation := &Occupation{OccID: tid, OccName: occp}
tsJSONasBytes, err := json.Marshal(occupation)
if err != nil {
return shim.Error(err.Error())
}
//Putstate
err = stub.PutState(user, tsJSONasBytes)
if err != nil {
return shim.Error(err.Error())
}
// indexed and saved
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tsKey, err := getTSKey(stub, occupation.OccID, occupation.OccName)
if err != nil {
return shim.Error("Error getting Occupation key" + err.Error()
)
}
fmt.Println(tsKey)
value := []byte{0x00}
stub.PutState(tsKey, value)
fmt.Println("--- end submit Occupation successfully ---")
} else {
tsid = tsid + 1
}
}
return shim.Success(nil)
}
Figure 15: Smart contract to add occupation
The data is added into a struct. A struct is a structure of how the data captured is stored. Figure 16
shows how the structure of data is stored in blockchain.
type Occupation struct {
OccID string `json:"occID"`
OccName string `json:"occupation"`
Keywords []string `json:"keywords"`
Skillset []string `json:"skillset"`
}
Figure 16: Data structure stored in blockchain
5.2 Digital Footprint Using Blockchain
The processed skills and competencies data that is stored in the blockchain is immutable. This ensures
any illegal updates performed will be disregarded as it cross references with other nodes to check if the
data is the same or not. This is because, if the data is changed, the data has will change too. This will
disconnect from the original chain of blocks itself. This ensures the integrity of the data.
A digital footprint is established using blockchain. Blockchain traces records from the beginning to the
end (Blockchain Network, 2019). This is enabled using the block hash which are chained together.
Using this feature, any data created or updated in the blockchain leaves a trace of who has invoked the
call as it stores the identity of user too. Not only does the blockchain trace the state of the data, it also
traces the person who invokes it.
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Figure 17: Digital footprint in blockchain
Figure 17 shows how the blocks are chained together. Each update or adding of data creates a data hash
and stores the person to has invoked the function. This ensures the digital footprint of each processed
skills and competencies data.
5.3 Processed Data Retrieved from Blockchain for Comparison
The processed skills and competencies data that is stored in the blockchain will be retrieved by the
system to be compared with the skills and competencies database of the organisation. Since the integrity
of the processed data is maintained, it will be used to compare with that of the organisation. Figure 18
shows how the data is retrieved from the blockchain for comparison
Figure 18: Process flow of data retrieval from blockchain flow
A data query call is sent to the blockchain API requesting the specific data. This request is then routed
to any of the peers in the blockchain network. The requested query is then searched in the blockchain
network, if it exists or not. If it exists, the verified data is then returned to the application for its use.
A function in the smart contract allows the data retrieval using the occupation keyword. This will return
the latest data related to the occupation. Figure 19: Function to retrieve data from blockchain shows
the smart contract used to retrieve data related to the occupation:
Data is retrieved
using a query call
from the
blockchain API
Query call is
directed to a peer
Data is verified
with signed
blocks
Processed data is
returned to the
application, if
exist
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func (m *AIOSGChaincode) searchKeyword(stub shim.ChaincodeStubInterface, args
[]string) pb.Response {
queryString := "{"Keywords":"+"args[0] "+"}"
queryResults, err := getQueryResultForQueryString(stub, queryString)
if err != nil {
return shim.Error(err.Error())
}
return shim.Success(queryResults)
}
Figure 19: Function to retrieve data from blockchain
6 Generated Occupational Skills Reference
A sample table of related knowledge and skills generated by the AIOSG system for a particular OSR is
given below. It shall include occupational structure, occupational area, competency levels, competency
profile, competency based curriculum, and guidelines for assessment and training. With more sources
of information from the Internet in the form of unstructured data and Government and private sector
databases in the form of structured data as well as verification and subject matter knowledge from
industry experts and lead bodies, the final output will be generated quickly and more accurately reflect
the actual real-world knowledge and skills.
OSR TITLE Cybersecurity Penetration Tester
REQUIRED
ACTIVITY
RELATED KNOWLEDGE RELATED SKILLS
Manage IP
Network
1.1 Network documentation and change
management
1.2 IP protocol stack layers including:
Role of a layered protocol stack
Key functions of each layer of
the IP stack
1.3 Ethernet operation and addressing
structure including:
Ethernet operating principles
Ethernet frame structure &
frame fields
MAC address structure
1.4 Transport layer protocols including:
TCP and UDP
TCP flow control
1.5 Network devices including:
Router
Switch
Networking Interface
1.6 IP network management tools and
software
Protocol analyser
Command line
1.1 Configuring and operating between
layers of the IP protocol stack
1.2 Interpret Ethernet features and
operations, configuration and
troubleshooting
1.3 Interpret key transport layer protocol
operations and act on them
1.4 Identifying key network devices and
their attributes
1.5 Connecting to key network devices
1.6 Utilisation of suitable tools and
software for managing IP networks
18. E-PROCEEDING OF THE 8TH INTERNATIONAL
CONFERENCE ON SOCIAL SCIENCES RESEARCH 2019
E-PROCEEDING OF THE 8TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES RESEARCH (ICSSR 2019).
(e-ISBN 978-967-0792-36-1). 18-19 November 2019, Imperial Heritage Hotel, Melaka, Malaysia.
Organised by https://worldconferences.net Page 46
7 Conclusion
This paper presents a system to address the gaps in generating occupational skills reference (OSR)
documents for the workforce. The system crawls for data from the Internet and other relevant databases
in addition to taking account of inputs from industry experts. This is to form a complete picture in terms
of inputs toward a particular job sector. The inputs are then processed in terms of the relative nature of
the skills to a particular job by way of Artificial Intelligence. The processed data is then verified by the
industry expert and lead bodies’ panel to generate the final output. The final OSR is stored in the
blockchain network to ensure traceability in terms of updates of such records and be put up for review
by a panel of experts for decision-making, publishing and future workforce planning. Malicious parties
cannot easily tamper the records of the documents and associated materials, due to the immutable nature
of blockchain. Through the enablement of such a system, relevant and applicable occupational skills
can be utilised by various industries.
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