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OG0-091 TOGAF 9 Part 1 learning |

OG0-091 learning - TOGAF 9 Part 1 Updated: 2023

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Exam Code: OG0-091 TOGAF 9 Part 1 learning November 2023 by team

OG0-091 TOGAF 9 Part 1

TOGAF® 9 Part 1 Exam

Exam Summary

Exam Name: TOGAF® 9 Part 1 Exam

Exam Number:

OG0-091 - English

OG0-094 - Brazilian Portuguese

OG0-096 - Simplified Chinese

OG0-F91 - French

OG0-S91 - Latin American Spanish

Qualification upon passing: TOGAF 9 Foundation (and partial credit towards the TOGAF 9 Certified qualification)

Delivered at: Authorized Examination Provider Test Centers and via Online Proctored/

Prerequisites: None

Supervised: Yes

Open Book: No

Exam type: Multiple choice

Number of questions: 40

Pass score: 55% (22 out of 40 questions)

Time limit: 60 minutes (*)

Retake policy: If you fail the test you must wait one month before another attempt

Examination Fee: See Fees

Recommended Study: A Study Guide is available. The practice test included with the Study Guide is also available on its own.
TOGAF 9 Part 1
The-Open-Group TOGAF learning

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OG0-091 TOGAF 9 Part 1
OG0-092 TOGAF 9 Part 2
OG0-093 OG0-093 TOGAF 9 Combined Part 1 and Part 2
OG0-061 IT4IT Part 1

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TOGAF 9 Part 1
Question: 269
Which one of the following best describes the implications of TOGAF being a generic framework?
A. The organization must utilize an architecture tool in order to tailor the templates for use
B. It must be adapted to satisfy organization specific requirements
C. It can be utilized by most enterprises without further customization
D. It can only be used for enterprise level architecture projects
E. It should only be employed under the supervision of highly trained consultants
Answer: B
Question: 270
Which of the following is the architecture domain that describes the logical software and hardware capabilities?
A. Application Architecture
B. Business Architecture
C. Data Architecture
D. Technology Architecture
Answer: D
Question: 271
Which section of the TOGAF document describes the processes, skills and roles to establish and operate an
architecture function within an enterprise?
A. Part II: Architecture Development Method
B. Part III: ADM Guidelines and Techniques
C. Part IV: Architecture Content Framework
D. Part VI: TOGAF Reference Models
E. Part VII: Architecture Capability Framework
Answer: E
Question: 272
Which one of the following is NOT an element of an architecture framework?
A. A common vocabulary
B. A list of recommended standards
C. A method for designing an information system in terms of building blocks
D. A set of structuresWhich can be used to develop a broad range of architectures
E. A system development lifecycle method for software engineering
Answer: E
Question: 273
Which one of the following describes classification methods for architecture and solution artifacts within the
Architecture Repository?
A. Architecture Landscape
B. Architecture Vision
C. Enterprise Continuum
D. Governance Log
E. Standards Information Base
Answer: C
Question: 274
Which one of the following statements about the structure of the TOGAF 9 document is true?
A. Part I describes the TOGAF approach to Enterprise Architecture
B. Part II describes the definitions of terms used and the changes between versions of TOGAF
C. Part III describes requirements management and is considered to be the core of TOGAF
D. Part IV describes the ADM: a collection of guidelines and techniques used in TOGAF 9
Answer: A
Question: 275
According to TOGAF, Which one of the following best describes an enterprise architecture?
A. An architecture of a commercial organization
B. An architecture that consists of more than one subsidiary company
C. An architecture that crosses multiple systems, and multiple functional groups within the enterprise
D. The highest level of architecture that can be achieved in a given organization
Answer: C
Question: 276
In TOGAF, What is the difference between an artifact and a deliverable?
A. An artifact contains one or more deliverables
B. Artifacts and deliverables are synonymous; there is no difference between them
C. Deliverables are prepared by the Project Manager, whereas artifacts are defined by the Architect
D. Deliverables are reusable, whereas artifacts are unique to a given architecture project
E. Deliverables are specified as contractual outputs from a project, whereas artifacts are not
Answer: E
Question: 277
Which one of the following lists the main components within the TOGAF Architecture Repository?
A. Organizational Metamodel, Architecture Capability, Architecture Landscape, Best Practices, Reference
Library, Compliance Strategy
B. Architecture Metamodel, Organizational Capability Model, Application Landscape, SIB, Reference
Library, Governance Model
C. Business Metamodel, Architecture Capability, Architecture Landscape, SIB, Reference Library,
Governance Log
D. Architecture Metamodel, Architecture Capability, Architecture Landscape, SIB, Reference Library,
Governance Log
Answer: D
Question: 278
According to the TOGAF Document Categorization Model, Which category describes a technique that is
referenced by processes categorized as TOGAF Core and TOGAF Mandated?
A. TOGAF Guidelines and Techniques
B. TOGAF Recommended
C. TOGAF Supporting
D. TOGAF Extension
Answer: B
Question: 279
Which of the following reasons best describes why the ADM numbering scheme for versioning output is an
example and not mandatory?
A. To show the evolution of deliverables
B. To permit adaptation as required
C. To enable use with the Architecture Content Framework
D. To support change management
Answer: B
Question: 280
According to TOGAF, Which of the following are the architecture domains that are commonly accepted subsets of
an overall enterprise architecture?
A. Application, Business, Data, Technology
B. Capability, Segment, Strategic
C. Context, Definition, Governance, Transformation
D. Definition, Realization, Transition, Vision
Answer: A
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The-Open-Group TOGAF learning - BingNews Search results The-Open-Group TOGAF learning - BingNews Revolutionizing Enterprise Architecture: How Wateen Telecom Champions TOGAF Principles No result found, try new keyword!In the dynamic world of enterprise IT, the Open Group Architecture Framework (TOGAF) stands as a beacon of excellence. TOGAF, with its multi-layered ... Tue, 07 Nov 2023 22:44:00 -0600 en-US text/html The Learning Network No result found, try new keyword!What can we learn from their remarkable friendship? By The Learning Network We invite students to play critic and submit an original review about a accurate creative work. Contest runs from Nov. 1 ... Wed, 15 Nov 2023 18:00:00 -0600 en text/html Open Learning Resources

Open Learning initiatives and programs provide anyone with access to the Internet with the ability to learn about virtually any subject. All of these programs are free and readily available to anyone who wants to learn, regardless of location and prior knowledge or education. They vary in from lectures to genuine online classes with homework, and some classes even offer certificates upon completion of a course.


Udacity provides free quarterly courses in mathematics, science and technology. During each quarter users can sign up for any number of courses they choose. The professors at Udacity are qualified volunteers who provide lectures and homework throughout the course, as well as a certificate once the courses is completed.

Carnegie Mellon University

The Carnegie Mellon University Open Learning initiative is a service that offers free online courses. These courses are taught by qualified educators who aim to fuse research, high quality courses, and student and instructor feedback to Boost secondary education.

Khan Academy

Khan Academy boasts over 3800 videos on a wide variety of Topics ranging from mathematics to world history to chemistry and finance. Videos are all available, with no sign-up required, however, registering account enables user to earn badges, track their learning progress and stats and access a unique, adaptive learning environment that changes according the user's individual pace and needs.

Harvard University

The Harvard Extension School provides students with an opportunity to take courses, earn certificates, or work toward a degree.

Queen's University Belfast

Queens University Belfast’s Open Learning Program offers a wide variety of part-time courses that are open to all adults, regardless of qualifications or experience. These courses are offered during the day, evenings and weekends and 3 programs are available each academic year.

University of Guelph

The Open Learning program at the University of Guelph is a distance-only mode of study that provides individuals with access to degree-credit university courses. These courses are for individuals who are interested in personal enrichment, professional updating, or eventual application to a degree program.


Coursera offers free online courses from some of the top universities in the world with certificates available upon completion of some courses.

Yale University

Open Yale Courses are open, free introductory courses that are taught by Yale teachers and Scholars.

Stanford University

The site provides a variety of learning options. There are 16 free classes.

Fri, 11 Feb 2022 16:11:00 -0600 en text/html
Thani Sokka

Enterprise Cloud Platform Strategic Account Manager


Thani Sokka has over 17 years of experience in systems engineering, enterprise architecture, design and development, software project management, and data/information modeling, working with the latest IT systems technologies and methodologies. He has spent significant time designing solutions for the public sector, media, retail, manufacturing, financial, biomedical, and social/gaming industries. At Google, Thani is a Strategic Account Manager focused on empowering Google Cloud Platform’s largest customers derive the most from Google’s cloud technologies, including it’s compute, storage, and big data solutions. He also works closely with the Google Cloud Platform Product Management and Product Engineering teams to help drive the direction of Google's Enterprise Cloud Platform business. Prior to Google, Thani was an enterprise architect at Oracle focused on helping Federal organizations implement SOA (Service Oriented Architecture) solutions. Thani also worked as a senior IT consultant at Booz Allen Hamilton, a lead software architect at Thomson Reuters, and a software engineer at MicroStrategy. Thani has achieved various IT certifications from organizations such as MicroStrategy, Oracle, and The Open Group (TOGAF). He holds a M.S. degree in Computer Science from Johns Hopkins University and a B.S. degree in Computer Science, Biomedical Engineering, and Electrical Engineering from Duke University.

Fri, 06 Mar 2015 18:37:00 -0600 en text/html
Study Group Guide


  1. The Goal of Study Group Learning
  2. Benefits of Study Group Learning
  3. What to do at Your First Meeting
  4. Your Role in Your Group
  5. Common Problems and Possible Solutions
  6. Study Group Locations at UMass Lowell

The Goal of Study Group Learning

It is believed that students learn by doing. As opposed to being spoon-fed knowledge in lecture, study groups encourage students to go above and beyond what is being taught and to develop their own understanding of subject material. The goal of study group learning is to help students take ownership of course material; to learn to learn.

Benefits of Study Group Learning

  • You can verify with each other any confusing or complex subject material.
  • Learning math is more fun.
  • Math is better understood and retained.
  • Prof/TA will be seen as more approachable.
  • You will have a chance to dialogue with classmates and therefore opportunity to make friends; hopefully you will feel less isolated.
  • Fellow students can be a source of encouragement.
  • Math-anxious students will see themselves as tutor/teachers, not just recipients of someone else's knowledge.
  • An increase in confidence of mathematical ability.
  • You will have opportunity to learn new study habits from peers. In a nutshell, learning math is more personally relevant, and intellectually stimulating.

As you can see from this list, being in a study group can be really helpful. BUT these benefits come only to those who are serious about making their group work well together and serious about learning. So, although I expect you to have fun (eh, there is no reason why you can't have a study group meeting at a pub once in a while) the study group component of your course is serious stuff.

Are you interested in joining a group but don't know how to start? Talk to your classmates or instructor.

What to do at Your First Meeting

We hope that, 10 years from now, you will look back upon this first meeting of your study group with good sentiments. We realize that not all of you will form those "college-days" relationships that TV glamorizes, but some of you will. You just never know what might happen. Here are some things that will help get started on the right track:

Check off this list as you go.

  • Fill out the form called: Study Group Roster with your group members names, e-mail addresses, and phone numbers. Guard this sheet. You never know when it will come in handy (or, put the information in your funky address book...just make sure you have it.)
  • Get to know one another. How? Well, ask each other what your majors are or where you are from or how much you love to work in groups...etc. Make sure you have fun.
  • You may want to look around at the rooms that are suggested in this booklet for your study groups. You are going to spending a lot of time in these places (or wherever you hold your meetings).
  • Establish when and where you will hold your next meeting. In fact, I would advise you to decide exact dates for some of your meetings. Make dates--solid dates--right up until the end of term. This way, you will be sure to be able to keep these dates.
  • Establish a network by which you can contact each other for impromptu meetings.
  • Talk more. give your group a name if you want.

What to Do at Other Meetings

Below is a list of things you should/can do at your meetings. Suggestions in bold I would recommend you do at every meeting.

  • Establish what you hope to accomplish at your meeting.
  • Review lecture notes together, discuss anything you did not understand.
  • Discuss key concepts from lecture.
  • Work on assignments.
  • Assign yourselves questions and work on them.
  • Study for tests or exams.
  • Discuss what questions you expect to be on tests and exams.
  • Go over copies of past exams.
  • Set aside time to talk about anything under the sun other than math.
  • At the end of each session, determine or verify location and time of you next meeting.

Your Role in Your Group

This page contains a section called Problems and Solutions. It's a good idea to read it so you know what can happen and so that you can recognize it when a problem arises. A lot of the problems can be prevented if you work hard not just at the math, but at making your group work. Below is a list of guidelines that, when followed, help to avoid the common pitfalls.

  • Listen carefully to each other. Try not to interrupt. Respond to, or at least acknowledge, comments made or questions asked by other group members. To do so shows respect.
  • Do not accept confusion passively. If you do not understand the information that someone is presenting, try to paraphrase what was said, or ask someone to help you paraphrase it.
  • Ask for clarification whenever someone uses a word in a way that you find confusing (you will likely help him or her.) The correct use of terminology is an essential part of successful communication in math. If you can say it, you understand it.
  • Do not split up the work. I know it's so tempting...but it leads to SOOO many problems. Everyone should focus their attention on the same thing at the same time. It is much easier to resolve conflicts when group members work together and check for agreement frequently.
  • Make a habit of explaining your reasoning or of "thinking out loud," and ask others to do the same. The process of constructing and refining explanations helps everyone to relate the information being presented to what they already know.
  • Be aware of time constraints. It is appropriate (and important) to ask each other how what you are doing will help your group complete an assignment
  • If your group gets stuck, review and summarize what you've done so far. This process creates new opportunities for group members to ask questions, and often it will reveal important connections that have been overlooked.
  • You should always feel comfortable to switch from talking about math to talking about how your group is working. If you have a concern about how your group is functioning, bring it up. Be direct, honest and calm.
  • When someone raises a concern about your group, listen to it carefully. If you have a problem with his or her problem, be sure to criticize the problem and not the person.

Common Problems and Possible Solutions

There are some problem situations in which a study group might find itself. Occasionally a group just doesn't have any interaction among its members. More frequent is the problem of one student is doing all the work, either because no one else will or because he or she doesn't trust the other group members to do a good job. These problems undermine the whole idea behind study groups and are actually detrimental to learning. Not to over-dramatize this but: BE AWARE!!! Just knowing about what can happen helps to prevent or nip in the bud serious problems.

The following list is offered to increase your awareness of potential problems as well as to offer advice on how to deal with them. We cannot stress enough how important it is to discuss your problems in your group. This is why it is so important to establish an open working environment in which you can be objective about how things are going in your group and comfortable enough to point out problems.

Problem: Lack of interaction

Possible Cause: Lack of Experience with Learning in Study Groups


  • Talk about the problem.
  • As a group, review the section of this booklet entitled, "Your Role in Your Group." You may find that you have not been following one of the guidelines. For example, the impulse to split up the work just kills group interaction.
  • I know this sounds a little corny, but maybe you need to practice how to learn in your groups. Forget about math for the moment and try this activity: choose a really hot issue like abortion or immigration. Let someone in your group make a clear statement about a point of view (it does not have to be their own point of view). Then another person has to counter that opinion without criticizing the first person. Interruptions are not allowed. Go around in a circle until the issue is exhausted.

Possible Cause: You Feel Coerced to Participate


  • Recall that your study group is for your benefit. If your study group isn't serving your needs you should discuss the matter in your group. Try to come up with strategies for making the group work better for you. Remember, your primary obligation is to yourself and to your group members, not to your professor, UMass Lowell, or some set of rules.

Possible Cause: Physical Arrangement


  • Make sure you are all sitting facing each other and no one feels isolated by the seating arrangement. When your meeting starts, mentally put yourself in the place of other group members. Is there anyone who you think might feel cut off from the group?

Problem: Group members are participating unequally

Possible cause: Intolerance of Silence

Some people feel a strong need to fill in moments of silence with speech. In the same way that nature abhors a vacuum, some people abhor silence in conversations.


  • Ask for silence. You could cut into someone's aimless babbling by saying
    "Wait a second! I need some space to think about this!"
  • Bring up the problem carefully with the person one-on-one. Often people who rush into a conversation to keep it going don't realize they are doing it. Explaining that you like long pauses in conversations in order to think could solve the problem.
  • Make sure you are not talking non-stop.

Possible Cause: Dominant Speakers Monopolize the Discussion.


  • Talk to dominating speakers privately. Find out why they are talking so much and whether they are aware of the problem. Often such people are simply outgoing by nature and are unaware of the problem. If they are aware of the problem, usually they feel they have not had their point fully appreciated, or they feel some need to compete. In all three cases, just talking to them in private can reduce the need for them to dominate conversations.

Possible Cause: A Group Member Has No Interest in Speaking.

Some students feel that they learn better by listening than by talking. Others feel that speaking and helping others requires too much effort.


  • Try to draw the person into the conversation without being obvious about it. Occasionally ask for their opinion.
  • Make it clear that you appreciate what the person does contribute.

Problem: A Group Member is Doing All the Work

Possible Cause: Lack of Trust between Members to Work


  • Tell the group member directly that you would like to help. Interrupt the group member with an "excuse me," and voice your ideas. Sit beside the group member and give your opinions about what he or she is doing. Question this group member about what he or she is doing.
  • Talk to the group member in private. Explain how you want to help with the work. After a private discussion with the person, he or she may come to view you as a serious student and open up to your ideas. Demonstrate cooperative learning to this student by involving the rest of the group in the work at hand.
  • If you are the group member who doesn't trust anyone else to do the work, then it is your responsibility to work with your group to make changes so that trust is reestablished. Although you may have a personal goal to complete assignments perfectly, it is a fundamental principle of study groups that everyone in the group understands the material presented in assignments.

Possible Cause: The Rest of Your Group is Slacking Off.


  • Don't be so eager to be the person who asks to get started. Wait until someone else takes the initiative to start the work. It may take a long time for someone else to pick up the reins, but after you do this a couple of times, the others will realize that they can no longer expect you to be the baby-sitter. Let them see the consequences of their inaction.
  • Enlist the help of other members either by asking them for it directly or by pretending you don't understand something. When they see that you can't do all the work on your own, they have to help.
  • Talk about your problem with your group as a whole. Say that you are ticked off (I realize that it takes a lot of courage to say that you are miffed— What if the rest of the group doesn't care that you are ticked off? Then again, maybe the only way for your group to care a little is for you show some emotion. Maybe they do not realize that you are upset.)

Possible Cause: A Group Member is the Brightest Student In the Group.


  • Talk to the brightest student. It may be as simple as saying "It's clear that you really get this stuff. But since we are all supposed to understand, going slower would help."
  • If you think you are head and shoulders above the rest of your group, then your task is particularly challenging. Although it is difficult, you must strike a good balance between contributing your own ideas and listening to others to ensure they understand your, their own, and others' ideas. One of the best ways to learn is by doing. Clearly, your group members need the opportunity to do mathematics. Make sure your zealousness doesn't take that away from them. Also, accept that there are many ways to approach a problem. Even when you know one solution to a problem, there is a lot to be learned from listening to alternative approaches. You also learn more by helping other students understand the material. Professors will tell you that they really began to understand math/stats well when they started to teach it!

Problem: A Group Member is Being Uncooperative.


  • Try talking to the group member in private. He or she may have a problem with how the group is being run but doesn't feel comfortable bringing it up with the whole group.
  • Hold a group meeting and invite your professor to discuss the problem.

Problem: Reinforcing Misconception

It is quite easy for a group of students to mistakenly agree, for example, that when they get zero over zero they can cancel to get one. Or to misread what it means for a function to be continuous. Or to confuse "if" with "only if." Who will be around to point out these errors?


  • Those who are sensitive to the careless use of language must make a practice of requesting clarification. If something doesn't sound right to you, say so. You may actually discover a wide-spread misconception.
  • If you are uncertain about what is correct or incorrect, have someone in the group ask the professor at the end of the next class or during their office hours. Have the phrase repeated to the professor. You could also ask a prof or tutor in OS219A.

Study Groups Locations at UMass Lowell

  • In Olsen Hall, the lobbies in the middle of floors 2 through 5 are large enough for a meeting.
  • Most departments have a lounge for their majors. As long a one member of your study group is a major in a department, it should be appropriate for the whole group to use the lounge for meetings. Mathematics and Chemistry majors share a lounge in Olney 407.
  • CLASS - Second floor of Southwick.
  • Basement of Southwick- good for meetings, particularly at mealtimes.
  • Off - campus
    • Places on University Ave. would love to have you as long as you buy a drink, at least. 
Tue, 22 Dec 2015 05:48:00 -0600 en text/html
Machine Learning

We explore and develop the capacity for algorithms to learn and make decisions and predictions from their environment. We follow a series of complementary approaches within the group, from biologically inspired computational models to probabilistic modelling and dimensionality reduction.

Research themes

Bioinspired Machine Learning

Inspired by how biological systems learn and make decisions we are developing computational models of the brain's own learning mechanisms. This leads to impactful results in the areas of supervised, unsupervised and reinforcement learning, and vice versa to impactful results of machine learning in neuroscience. Importantly this field can provide deep insight into biological systems, from high-level complex behaviour (such as modelling the navigation systems of insects and mammals and applications to robotics) to modelling low level synaptic dynamics. Deep learning, for instance, is an example of a successful machine learning method loosely based on biological neural networks.

Probabilistic Machine Learning

We develop probabilistic modelling techniques to produce predictions for ML challenges that require uncertainty quantification. In particular we look at using grey-box modelling approaches (such as Latent Force Models) for Gaussian process regression and classification, to allow efficient use of limited data. Other work includes developing approaches to provide differentially private predictions. Applications include temporal-spatial multi-fidelity modelling, gene-expression modelling, personalised medicine, air pollution sensor network calibration, differential-privacy and classifier robustness against adversarial-attack.

Dimensionality Reduction

We have developed dimensionality reduction approaches for a variety of datasets in particular around medical imaging and computer vision recommender systems. The core approach is through the application of tensor analysis for dimensionality reduction allowing highly complex high-dimensional-tensor structures to be analysed. Examples include several video applications (e.g. for epileptic seizure detection) and the analysis of volumetric medical imaging

Affiliated members


GPy - An open-source framework for Gaussian Processes (GP) written in Python.

GPyOpt - An open-source library for Bayesian Optimization using GPy, written in Python.

Rodent Data Analytics (RODA) - A MATLAB suite of algorithms and a software for analysis and classification of rodents trajectory data in the Morris Water Maze.

PyKale -  A PyTorch library that provides a unified pipeline-based API for knowledge-aware machine learning on graphs, images, texts, and videos to accelerate interdisciplinary research.

Sheffield Machine Learning Network

Our group coordinates the Sheffield Machine Learning Network. The aim of the network is to promote collaboration and to provide support for researchers and students who are working with or have interest in machine learning topics. It is open to anyone within the Sheffield University, and aims to foster an accessible environment. More details about the network are available.

Tue, 07 Nov 2023 21:23:00 -0600 en text/html
Failed to open the Group Policy Object on this computer

I use Group Policy Editor to configure a lot of settings on Windows 11 or Windows 10. Recently when I tried opening it from Run prompt or directly through Control Panel, I received an error stating—Failed to open the Group Policy Object on this computer. You might not have the appropriate rights — unspecified error. If you get the same error, then here is how you can quickly fix the issue and gain back access to the Group Policy Editor.

Failed to open the Group Policy Object on this computer

The message was surprising because I had not changed anything that could have resulted in the error message. When I navigated to C:\Windows\System32\GroupPolicy, it had all the policies intact, but the Group Policy Editor wasn’t working. So here is what I did to resolve the issue. Make sure that your user account has Admin privileges.

Rename Machine Folder

  1. Set Windows to show hidden files and folders
  2. Navigate to the Group Policy folder
  3. Select Machine folder, and press F2 to rename it
  4. Rename Machine to Machine.old
  5. It will prompt for admin permission.
  6. Click on the Continue button.
  7. After the folder is renamed, open Group Policy Editor by typing gpedit.msc in the Run prompt followed by pressing the Enter key.
  8. Group Policy Editor will launch without a problem.
  9. Go back to the C:\Windows\System32\GroupPolicy folder, and you should see a new Machine folder.
  10. Now whatever changes you will make will be available in this folder.

Failed to Open group Policy Object on this computer

There is one more way to fix this.

You can choose to delete all the files inside the Machine folder instead of renaming it. Windows will automatically recreate the required files when you relaunch the policy editor.

The reason behind Failed to Open Group Policy Object error

After going through Microsoft and Technet forums, I noticed some users reporting about the same, and one of them shared about the corruption of Registry.pol with Event ID 1096. The file stores Registry-based policy settings, which include Application Control Policies, Administrative Templates, and more. There was a log in the Event Viewer which pointed towards this corruption. The description stated:

The processing of Group Policy failed. Windows could not apply the registry-based policy settings for the Group Policy object LocalGPO. Group Policy settings will not be resolved until this event is resolved. View the event details for more information on the file name and path that caused the failure.

It affirms the user’s report, and what you can do is delete the Registry.pol file available inside the Machine folder, and launch Group Policy again.

Read: Computer policy could not be updated successfully, The processing of Group Policy failed.

I hope this helps you resolve the error.

Now read: How to reset all Local Group Policy settings to default in Windows 11/10.

Failed to Open group Policy Object on this computer
Mon, 04 Apr 2022 02:51:00 -0500 en-us text/html
Smithsonian Open Access

Welcome to Smithsonian Open Access, where you can download, share, and reuse millions of the Smithsonian’s images—right now, without asking. With new platforms and tools, you have easier access to more than 4.9 million 2D and 3D digital items from our collections—with many more to come. This includes images and data from across the Smithsonian’s 21 museums, nine research centers, libraries, archives, and the National Zoo.

What will you create?


Remixes by: Access Smithsonian, Amazon Web Services-Sumerian, Amy Karle, An Open Book Foundation, AstroNuts, Autodesk Tinkercad, Cesium, Chris Funk & N M Bodecker Foundation, Creative Commons, Duke University-MorphoSource, Georgetown University Maker Hub in Lauinger Library, Google Arts & Culture, The Khronos Group, MHz Foundation, Michael Joo, Matthew Putman, and James J. Williams III, Sketchfab, Smithsonian Center for Learning and Digital Access, Smithsonian Data Science Lab, Smithsonian Libraries & Museum in a Box, Wikimedia DC

The Smithsonian Open Access launch event is presented in partnership with:

Google Arts Culture

Data hosting provided by AWS Public Dataset Program


The Smithsonian Open Access launch event is presented in partnership with:

Google Arts Culture

Data hosting provided by AWS Public Dataset Program


Tue, 21 Feb 2023 21:50:00 -0600 en text/html
A group behind Stable Diffusion wants to open source emotion-detecting AI

In 2019, Amazon upgraded its Alexa assistant with a feature that enabled it to detect when a customer was likely frustrated — and respond with proportionately more sympathy. If a customer asked Alexa to play a song and it queued up the wrong one, for example, and then the customer said “No, Alexa” in an upset tone, Alexa might apologize — and request a clarification.

Now, the group behind one of the data sets used to train the text-to-image model Stable Diffusion wants to bring similar emotion-detecting capabilities to every developer — at no cost.

This week, LAION, the nonprofit building image and text data sets for training generative AI, including Stable Diffusion, announced the Open Empathic project. Open Empathic aims to “equip open source AI systems with empathy and emotional intelligence,” in the group’s words.

“The LAION team, with backgrounds in healthcare, education and machine learning research, saw a gap in the open source community: emotional AI was largely overlooked,” Christoph Schuhmann, a LAION co-founder, told TechCrunch via email. “Much like our concerns about non-transparent AI monopolies that led to the birth of LAION, we felt a similar urgency here.”

Through Open Empathic, LAION is recruiting volunteers to submit audio clips to a database that can be used to create AI, including chatbots and text-to-speech models, that “understands” human emotions.

“With Open Empathic, our goal is to create an AI that goes beyond understanding just words,” Schuhmann added. “We aim for it to grasp the nuances in expressions and tone shifts, making human-AI interactions more authentic and empathetic.”

LAION, an acronym for “Large-scale Artificial Intelligence Open Network,” was founded in early 2021 by Schuhmann, who’s a German high school teacher by day, and several members of a Discord server for AI enthusiasts. Funded by donations and public research grants, including from AI startup Hugging Face and Stability AI, the vendor behind Stable Diffusion, LAION’s stated mission is to democratize AI research and development resources — starting with training data.

“We’re driven by a clear mission: to harness the power of AI in ways that can genuinely benefit society,” Kari Noriy, an open source contributor to LAION and a PhD student at Bournemouth University, told TechCrunch via email. “We’re passionate about transparency and believe that the best way to shape AI is out in the open.”

Hence Open Empathic.

For the project’s initial phase, LAION has created a website that tasks volunteers with annotating YouTube clips — some pre-selected by the LAION team, others by volunteers — of an individual person speaking. For each clip, volunteers can fill out a detailed list of fields, including a transcription for the clip, an audio and video description and the person in the clip’s age, gender, accent (e.g. “British English”), arousal level (alertness — not sexual, to be clear) and valence level (“pleasantness” versus “unpleasantness”).

Other fields in the form pertain to the clip’s audio quality and the presence (or absence) of loud background noises. But the bulk focus is on the person’s emotions — or at least, the emotions that volunteers perceive them to have.

From an array of drop-down menus, volunteers can select individual — or multiple — emotions ranging from “chirpy,” “brisk” and “beguiling” to “reflective” and “engaging.” Noriy says that the idea was to solicit “rich” and “emotive” annotations while capturing expressions in a range of languages and cultures.

“We’re setting our sights on training AI models that can grasp a wide variety of languages and truly understand different cultural settings,” Noriy said. “We’re working on creating models that ‘get’ languages and cultures, using videos that show real emotions and expressions.”

Once volunteers submit a clip to LAION’s database, they can repeat the process anew — there’s no limit to the number of clips a single volunteer can annotate. LAION hopes to gather roughly 10,000 samples over the next few months, and — optimistically — between 100,000 to 1 million by next year.

“We have passionate community members who, driven by the vision of democratizing AI models and data sets, willingly contribute annotations in their free time,” Noriy said. “Their motivation is the shared dream of creating an empathic and emotionally intelligent open source AI that’s accessible to all.”

The pitfalls of emotion detection

Aside from Amazon’s attempts with Alexa, startups and tech giants alike have explored developing AI that can detect emotions — for purposes ranging from sales training to preventing drowsiness-induced accidents.

In 2016, Apple acquired Emotient, a San Diego firm working on AI algorithms that analyze facial expressions. Snatched up by Sweden-based Smart Eye last May, Affectiva — an MIT spin-out — once claimed its technology could detect anger or frustration in speech in 1.2 seconds. And speech recognition platform Nuance, which Microsoft purchased in April 2021, has demoed a product for cars that analyzes driver emotions from their facial cues.

Other players in the budding emotion detection and recognition space include Hume, HireVue and Realeyes, whose technology is being applied to gauge how certain segments of viewers respond to certain ads. Some employers are using emotion-detecting tech to evaluate potential employees by scoring them on empathy and emotional intelligence. Schools have deployed it to monitor students’ engagement in the classroom — and remotely at home. And emotion-detecting AI has been used by governments to identify “dangerous people” and tested at border control stops in the U.S., Hungary, Latvia and Greece.

The LAION team envisions, for their part, helpful, unproblematic applications of the tech across robotics, psychology, professional training, education and even gaming. Schuhmann paints a picture of robots that offer support and companionship, virtual assistants that sense when someone feels lonely or anxious and tools that aid in diagnosing psychological disorders.

It’s a techno utopia. The problem is, most emotion detection is on shaky scientific ground.

Few, if any, universal markers of emotion exist — putting the accuracy of emotion-detecting AI into question. The majority of emotion-detecting systems were built on the work of psychologist Paul Ekman, published in the ’70s. But subsequent research — including Ekman’s own — supports the common-sense notion that there’s major differences in the way people from different backgrounds express how they’re feeling.

For example, the expression supposedly universal for fear is a stereotype for a threat or anger in Malaysia. In one of his later works, Ekman suggested that American and Japanese students tend to react to violent films very differently, with Japanese students adopting “a completely different set of expressions” if someone else is in the room — particularly an authority figure.

Voices, too, cover a broad range of characteristics, including those of people with disabilities, conditions like autism and who speak in other languages and dialects such as African-American Vernacular English (AAVE). A native French speaker taking a survey in English might pause or pronounce a word with some uncertainty — which could be misconstrued by someone unfamiliar as an emotion marker.

Indeed, a big part of the problem with emotion-detecting AI is bias — implicit and explicit bias brought by the annotators whose contributions are used to train emotion-detecting models.

In a 2019 study, for instance, scientists found that labelers are more likely to annotate phrases in AAVE more toxic than their general American English equivalents. Sexual orientation and gender identity can heavily influence which words and phrases an annotator perceives as toxic as well — as can outright prejudice. Several commonly used open source image data sets have been found to contain racist, sexist and otherwise offensive labels from annotators.

The downstream effects can be quite dramatic.

Retorio, an AI hiring platform, was found to react differently to the same candidate in different outfits, such as glasses and headscarves. In a 2020 MIT study, researchers showed that face-analyzing algorithms could become biased toward certain facial expressions, like smiling — reducing their accuracy. More accurate work implies that popular emotional analysis tools tend to assign more negative emotions to Black men’s faces than white faces.

Respecting the process

So how will the LAION team combat these biases — making certain, for instance, that white people don’t outnumber Black people in the data set; that nonbinary people aren’t assigned the wrong gender; and that those with mood disorders aren’t mislabeled with emotions they didn’t intend to express?

It’s not totally clear.

Schuhmann claims the training data submission process for Open Empathic isn’t an “open door” and that LAION has systems in place to “ensure the integrity of contributions.”

“We can validate a user’s intention and consistently check for the quality of annotations,” he added.

But LAION’s previous data sets haven’t exactly been pristine.

Some analyses of LAION ~400M — a LAION image training set, which the group attempted to curate with automated tools — turned up photos depicting sexual assault, rape, hate symbols and graphic violence. LAION ~400M is also rife with bias, for example returning images of men but not women for words like “CEO” and pictures of Middle Eastern Men for “terrorist.”

Schuhmann’s placing trust in the community to serve as a check this go-around.

“We believe in the power of hobby scientists and enthusiasts from all over the world coming together and contributing to our data sets,” he said. “While we’re open and collaborative, we prioritize quality and authenticity in our data.”

As far as how any emotion-detecting AI trained on the Open Empathic data set — biased or no — is used, LAION is intent on upholding its open source philosophy — even if that means the AI might be abused.

“Using AI to understand emotions is a powerful venture, but it’s not without its challenges,” Robert Kaczmarczyk, a LAION co-founder and physician at the Technical University of Munich, said via email. “Like any tool out there, it can be used for both good and bad. Imagine if just a small group had access to advanced technology, while most of the public was in the dark. This imbalance could lead to misuse or even manipulation by the few who have control over this technology.”

Where it concerns AI, laissez faire approaches sometimes come back to bite model’s creators — as evidenced by how Stable Diffusion is now being used to create child sexual abuse material and nonconsensual deepfakes.

Certain privacy and human rights advocates, including European Digital Rights and Access Now, have called for a blanket ban on emotion recognition. The EU AI Act, the recently enacted European Union law that establishes a governance framework for AI, bars the use of emotion recognition in policing, border management, workplaces and schools. And some companies have voluntarily pulled their emotion-detecting AI, like Microsoft, in the face of public blowback.

LAION seems comfortable with the level of risk involved, though — and has faith in the open development process.

“We welcome researchers to poke around, suggest changes, and spot issues,” Kaczmarczyk said. “And just like how Wikipedia thrives on its community contributions, Open Empathic is fueled by community involvement, making sure it’s transparent and safe.”

Transparent? Sure. Safe? Time will tell.

Thu, 26 Oct 2023 11:59:00 -0500 en-US text/html
A group behind Stable Diffusion wants to open source emotion-detecting AI No result found, try new keyword!In 2019, Amazon upgraded its Alexa assistant with a feature that enabled it to detect when a customer was likely frustrated -- and respond with proportionately more sympathy. If a customer asked ... Fri, 27 Oct 2023 00:16:00 -0500 en-us text/html

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