Building a Delivery Team - by Bill DeRusha. Takeaway: How to leverage communication and tools like team charters and retrospectives to form a new team effectively and guide it to deliver on time, even on a tight schedule.
Communication Training Tomorrow! (no, not really) - by Jen Bunk. Takeaway: Commmunication trainings, and communication skills, aren’t enough to fix cultural issues. You need to go deeper and explore where your systems are failing.
Driving Change: Why Are Your Ideas Being Rejected? - by Bartosz Ocytko. Takeaway: A very thorough examination of the many different reasons why you might not be getting across, with guidance on determining which problems are important; how to get organizational support; bringing solutions instead of problems; timing, and what to do if despite your efforts change still doesn’t come.
11 Top Tips for a Successful Technical Presentation - by Scott Hanselman. Takeaway: detail-oriented tips ranging from font type/size to being mindful of your distracting personal habits.
Escaping E-mail Hell - by Julie Zhuo. Takeaway: Tips for communicating more succinctly in emails to reduce workload and also increase the chances that others will read them.
Etsy’s Charter of Mindful Communication - by Lara Hogan and Etsy’s Culture & Engagement team. Takeaway: When engaging with someone, keep it REAL—“Reflect on the dynamics in the room; Elevate the conversation; Assume best intentions; Listen to learn.”
42 Things Non-Front-Liners Misunderstand - by John Cutler. Takeaway: “It is unfair to expect people who haven’t worked on the front lines in software development to just get it. Just as, I’m guessing, it is hard for developers and UXers to grok sales and marketing, being a CEO, and working in HR. Assuming malice is a big mistake. We all want great outcomes for our respective businesses, but have a hard time bridging the divide.”
The Heartbeat Interview with Amy Gallo (video) - by Claire Lew. Takeaway: A chat about how leaders can become friendly and human with their teams, set boundaries and be friendly. Being human at work builds empathy and care.
High Agency (Twitter thread) - by Shreyas Doshi. Takeaway: a 20-tweet breakdown of High Agency, “a prerequisite for making a profound impact in one’s life & work.”
How to Blast Through Silo Mentality to Create a Culture of Experimentation - by Lindsay Kwan. Takeaway: Align your stakeholders on a vision for experimentation; get buy-in from stakeholders and leaders; clarify your experimentation protocol; get strategic with cross-functional teams; communicate.
How to Have Impact - by Vishal Kapur. Takeaway: “Some aspects of the impact generation process are in your control, and some aren’t. The world (i.e. the”environment“) isn’t in your control. Neither are the outcomes you get from trying things – remember the intention/impact gap. However, generating ideas, making things, taking risks, and the people you choose to work with are.”
How to Improve Emotional Intelligence - by Darius Foroux. Takeaway: Identify, interpret, manage your emotions. “When you can identify your own emotions, you will also get better at identifying other people’s emotions.” This can positively impact your leadership abilities.
How to Influence Culture When You’re Not the CEO - by Claire Lew. Takeaway: Model what you’d like to be true of your team; speak up with improvement ideas through the prism of team instead of individual/personal; give space, grace, and gratitude to leaders.
How To Lead When You Have No Authority - by Matt Russell.  In other words, leadership comes from influence and not from your position. For this reason, even when you’re not in front, you’re still leading those around you.”
How to Make Your Engineering Team More Effective - by Edmond Lau. Takeaway: Gather input as to what’s hard or frustrating; explicitly design how to align someone’s growth goals with what creates value; give and request frequent and honest feedback; leverage your strengths to level up the team; reduce sources of complexity, and create more opportunities for collaboration.
How to Win Hearts & Minds - Lessons Learned from Electoral Politics (video) - by K. Gray & C. Young. Takeaway: This GOTO Amsterdam talk connnects methods used in political campaigns to their useful application in a tech organization, so that you can connect with teams and influence them. You have to blend facts with emotional resonance.
The Indispensable Document for the Modern Manager - by First Round. Takeaway: “To grow the company and as a leader, every manager at a startup needs to scale herself. At the end of the day, writing a user guide is an exercise in self-awareness. What teams want from their leaders more than anything else is predictability and authenticity. This leads to trust. With trust, you can be unstoppable. Give yourself a leg up and take the time to self-reflect, write and revise a user guide. If your founding team and early hires are friends or past colleagues — this is especially critical.”
It’s Not Them, It’s You - by David Oates. Takeaway: “You’re in your first role as a leader and you’re not getting the results you expect — what do you do? Start by assuming it’s not them, it’s you.”
Just Because You Said It, Doesn’t Make It So - by Jeff Weiner. Takeaway: “We all need to be wary of avoiding the Ron Burgundy syndrome: On the surface, looking and sounding the part, but without providing the right discipline, focus, and ongoing context, appearing as nothing more than a talking head.” Includes lessons for communicating more effectively.
Leadership Is Not About Your Good Intentions - by Johnathan Nightingale. Takeaway: “If you want to be a leader, own it. Like you would anywhere else in your life. Work harder to understand the context. Develop better strategies for anticipating future failure and avoiding it. The stuff you expect junior folks to do.”
Leading by Speaking (video) - by Lara Hogan. Takeaway: “In our work, we each have moments of saying some prepared words under a spotlight - whether it’s during team standups, giving a presentation to a client, or pitching your promotion to your boss - and yet we all have different fears about those moments. In this talk, we’ll walk through the spectrum of public speaking fears and learn tactics to address them, so you can feel confident and equipped to step into the spotlight.”
Making Engineering Team Communication Clearer, Faster, Better - by First Round. Takeaway: Using a technical design review system with design documents can help teams communicate effectively and clearly. The article links to a design doc template by Google, and a completed example.
Management and False Certainty - by Abe Winter. Takeaway: “Even healthy people can APPEAR to have … bad traits when you’re not holding up your end by fighting for your beliefs, i.e. your skills and organizational knowledge, i.e. your spider-sense.”
Master the Art of Influence — Persuasion as a Skill and Habit - by FirstRound. Takeaway: “To persuade someone, you need to speak as much as you can to System I [of the brain]— the child, the interns — who want to believe you (because it just makes so much darn sense, what’s not to love?). Trouble is, most tech operators express themselves with complexity, nuance, facts and figures. That’s their default, and it doesn’t appeal to people’s unconscious processor.”
Meeting Resistance and Moving Forward - by Linda Rising. Takeaway: A keynote talk featuring tips for handling naysayers and skeptics.
Never Split the Difference Cheat-Sheet - by Yan-David Erlich. Takeaway: a detailed, six-page summary of Chris Voss’ Never Split the Difference, the 2016 book about negotiation. Includes blog post.
9 Tips: How IT Leaders Can Build Better Relationships - by Carla Rudder. Takeaway: Be a partner, be transparent, be truthful, be in constant communication, be OK with being wrong, be in the moment, be engaged, be a team player, be candid.
The Patient Change Agent - by John Cutler. Takeaway: “At a minimum, ask someone: Can you describe [to] an elephant in the room that I will quickly encounter, will think is totally fixable, but will be wrong? Why is the status quo difficult to change?”
Prattfalls: Better Communication - by Roy Rapoport. Takeaway: “Communication only exists as a mutation of someone else’s internal state; focus entirely on what mutation you’re trying to accomplish; be thoughtful about how to accomplish that mutation; validate THAT mutation occurred.”
Public Speaking Resources - by VM Brasseur. Takeaway: A collection of resources on speaker training/tips, technical conferences, proposing talks and more.
The Science of Speaking is the Art of Being Heard - by First Round. Takeaway: “Communication is not just about what you say, it’s about the reaction it causes in the listener. Often we think delivering a message is enough without checking to see if it was actually received. This can be made more efficient with attention to meta models. Lastly, communication is making sure there’s enough shared context for the person to act on what’s next.”
Stop Asking Your Employees This One Question — It’s Hurting Them - by Claire Lew. Takeaway: The question is, “How can I help you?” This is lazy, puts pressure on the employee, and is vague. Instead, “[p]oint out your own potential flaw, instead of waiting for your employee to point it out. Offer a critique of your own actions, instead waiting to see if it’s something your employee brings up.”
Uniquely HR - by Gary Ford. Takeaway: Influence is the power to affect someone/something without directly forcing them. It’s about establishing credibility, finding common ground, and connecting emotionally. A systemic approach to influencing.
Use Coaching Techniques to Help People Make Their Own Decisions (video) - by Meredith Noble. Takeaway: A talk about how to use the GROW coaching technique “to structure conversations and help people make their own decisions; ask questions that encourage people to consider new approaches; embrace the coaching mindset, and and let go of the urge to play expert.”
Wait But Why’s Tim Urban on Parsing and Transmitting Complex Ideas - by FirstRound Capital. Takeaway: “The first step is to identify what type of complexity you are attempting to unravel…Let’s call them: complexity as gathering, complexity as dusting and complexity as pattern-matching (or pattern-resisting)…[then P]inpoint where on a 1-10 scale the recipient of your explanation falls.”
Yes! And - by Tom Critchlow. Takeaway: “[I]n this theatre of human work it’s crucial to speak up. Spending time on the performance is not wasted - in fact quite the opposite. Without performance work gets sidelined, ignored or worse[.]”


I was hoping someone could help me translate this chr. from PRC to regular NWN for a PW. I was thinking of replacing the Fist of Raziel to a Paladin but I would like to see what others think and how would you go about and making the chr to fit some what close to the PRC version.
It might help if you posted the specs on the PRC version. _________________ It's rogue, dammit, ROGUE!!!
Quote: Posted 09/27/05 15:21:54 (GMT) -- Cinnabar Din
It might help if you posted the specs on the PRC version.
The Holy Paragon is one of the Bioware Epic Character Builds. It's up on the website.
Steve
Thanks, Steve. I haven't looked at those builds for so long that I forgot it was one of them. Apologies to the OP. _________________ It's rogue, dammit, ROGUE!!!
i was trying to think of a way to convert it to regular NWN for a PW....thanks for clearing up the specs stuff and where to find it i should have stated that....
plz if you come up with a good conversion let me know with a post


Long List item ( with or without word spacing) are wrapped into multi line. Width are fixed to 150px.
<am-multiselect class="input-lg" multiple="true" ms-selected="There are {{selectedCar.length}} car(s) selected" ng-model="selectedCar" ms-header="Select Some Cars" options="c as c.name for c in cars" ms-id="id" list-css="fixed-width" list-item-css="word-wrapped" change="selected()" onchange="OnSelectionChange(event)"> </am-multiselect> With CSS: .fixed-width { width: 150px; } am-multiselect .dropdown-menu > li > .word-wrapped { word-wrap: break-word; !important; word-break: break-all; !important; white-space: normal; !important; }
<am-multiselect class="input-lg" ng-model="selectedFruit" options="c.name for c in fruits" change="selected()"> </am-multiselect>


To ensure delivery, add<EMAIL_ADDRESS>to your address book
It seems your forgot the password for your account. You can change your password by clicking the button below:
If you didn't mean to reset your password, then you can just ignore this email - your password will not change.
If you're having trouble clicking the password reset button, copy and paste the URL below into your web browser:
https://dummy.com/password-reset/AbCDeFGhIJKLm
Fermentum varius<EMAIL_ADDRESS>213 Vestibulum 24th blandit fringilla Consectetur 1929.
Terms / Privacy Policy


ADOBE
Software Licence Agreement
NOTICE TO USER: PLEASE READ THIS AGREEMENT CAREFULLY. BY COPYING, INSTALLING OR USING ALL OR ANY PORTION OF THE SOFTWARE YOU ACCEPT ALL THE TERMS AND CONDITIONS OF THIS AGREEMENT, INCLUDING, IN PARTICULAR THE LIMITATIONS ON: USE CONTAINED IN SECTION 2; TRANSFERABILITY IN SECTION 4; WARRANTY IN SECTIONS 6 AND 7; LIABILITY IN SECTION 8; AND SPECIFIC PROVISIONS AND EXCEPTIONS IN SECTION 14.   IF YOU DO NOT AGREE TO THE TERMS OF THIS AGREEMENT, DO NOT USE THE SOFTWARE. VISIT HTTP://WWW.ADOBE.COM FOR TERMS OF AND LIMITATIONS ON RETURNING THE SOFTWARE FOR A REFUND.
You may have another written agreement directly with Adobe (e.g., a volume licence agreement) that supplements or supersedes all or portions of this agreement.
Adobe and its suppliers own all intellectual property in the Software. The Software is licensed, not sold. Adobe permits you to copy, download, install, use, or otherwise benefit from the functionality or intellectual property of the Software only in accordance with the terms of this agreement. Use of some third party materials included in the Software may be subject to other terms and conditions typically found in a separate licence agreement or "Read Me" file located near such materials.
The Software may include product activation and other technology designed to prevent unauthorized copying. The activation technology may prevent your Use of the Software if you do not follow the activation process described in the Software and Documentation. Visit http://www.adobe.com for information about product activation.
1. Definitions.
"Adobe" means Adobe Systems Incorporated, a Delaware corporation, 345 Park Avenue, San Jose, California 95110, if subsection 10 (a) of this agreement applies; otherwise it means Adobe Systems Software Ireland Limited, Unit 3100, Lake Drive, City West Campus, Saggart D24, Republic of Ireland, a company organized under the laws of Ireland and an affiliate and licensee of Adobe Systems Incorporated.
"Computer" means a computer device that accepts information in digital or similar form and manipulates it for a specific result based on a sequence of instructions.
"Internal Network" means a private, proprietary network resource accessible only by employees and individual contractors (i.e., temporary employees) of a specific corporation or similar business entity. Internal Network does not include the Internet or any other network community open to the public, including but not limited to membership or subscription driven groups, associations and similar organizations.
"Permitted Number" means five (5) unless otherwise indicated under a valid licence (e.g., volume licence) granted by Adobe.
"Software" means (a) all of the information with which this agreement is provided, including but not limited to (i) Adobe or third party software files and other computer information; (ii) sample and stock photographs, images, sounds, clip art and other artistic works ("Stock Files"); (iii) related explanatory written materials and files ("Documentation"); and (iv) fonts; and (b) any modified versions and copies of, and upgrades, updates and additions to, such information, provided to you by Adobe at any time, to the extent not provided under a separate agreement (collectively, "Updates").
2. Software Licence. As long as you obtained the Software from Adobe or one of its authorized licensees and as long as you comply with the terms of this agreement, Adobe grants you a non-exclusive licence to use the Software in the manner and for the purposes described in the Documentation, as further set forth below. See Section 14 for specific provisions related to certain components.
2.1 General Use. You may install and use one copy of the Software on up to the Permitted Number of your compatible Computers; or
2.2 Server Deployment. You may install one copy of the Software on one Computer file server within your Internal Network for the purpose of downloading and installing the Software on up to the Permitted Number of other Computers within the same Internal Network; or
2.3 Server Use. You may install one copy of the Software on one Computer file server within your Internal Network for the purpose of using the Software through commands, data or instructions (e.g., scripts) from another Computer within the same Internal Network, provided that the total number of users (not the concurrent number of users) that are permitted to use the Software on such Computer file server does not exceed the Permitted Number. No other network use is permitted, including, but not limited to use of the Software, either directly or through commands, data or instructions, from or to a Computer not part of your Internal Network, for Internet or web hosting services or by any user not licensed to use this copy of the Software under a valid licence from Adobe; and
2.4 Portable or Home. The primary user of the Computer on which the Software is installed may install a second copy of the Software for his or her exclusive use on either a portable Computer or a Computer located at his or her home, provided the Software on the portable or home Computer is not used at the same time as the Software on the primary Computer.
2.5 Backup Copy. You may make a reasonable number of backup copies of the Software, provided your backup copies are not installed or used for other than archival purposes.
2.6 Stock Files. Unless stated otherwise in the "Read-Me" files associated with the Stock Files, which may include specific rights and restrictions with respect to such materials, you may display, modify, reproduce and distribute any of the Stock Files. However, you may not distribute the Stock Files on a stand-alone basis (i.e., in circumstances in which the Stock Files constitute the primary value of the product being distributed). Stock Files may not be used in the production of libelous, defamatory, fraudulent, lewd, obscene or pornographic material or any material that infringes upon any third party intellectual property rights or in any otherwise illegal manner. You may not claim any trademark rights in the Stock Files or derivative works thereof.
3. Intellectual Property Ownership. The Software and any authorized copies that you make are the intellectual property of and are owned by Adobe Systems Incorporated and its suppliers. The structure, organization and code of the Software are the valuable trade secrets and confidential information of Adobe Systems Incorporated and its suppliers. The Software is protected by law, including but not limited to the copyright laws of the United States and other countries, and by international treaty provisions. Except as expressly stated herein, this agreement does not grant you any intellectual property rights in the Software and all rights not expressly granted are reserved by Adobe and its suppliers.
4. Restrictions.
4.1 Notices. You may not copy the Software except as set forth in Section 2 and 14. Any permitted copy of the Software that you make must contain the same copyright and other proprietary notices that appear on or in the Software.
4.2 No Modifications. Except as permitted in Section 14.7, you may not modify, adapt or translate the Software. You may not reverse engineer, decompile, disassemble or otherwise attempt to discover the source code of the Software except to the extent you may be expressly permitted under applicable law to decompile only in order to achieve interoperability with the Software.
4.3 No Unbundling. The Software may include various applications, utilities and components, may support multiple platforms and languages and may be provided to you on multiple media or in multiple copies. Nonetheless, the Software is designed and provided to you as a single product to be used as a single product on Computers as permitted by Sections 2 and 14. You are not required to use all component parts of the Software, but you may not unbundle the component parts of the Software for use on different Computers. You may not unbundle or repackage the Software for distribution, transfer or resale. See Section 14 for specific exceptions to this Section.
4.4 No Transfer. YOU MAY NOT RENT, LEASE, SELL, SUBLICENCE, ASSIGN OR TRANSFER YOUR RIGHTS IN THE SOFTWARE, OR AUTHORIZE ANY PORTION OF THE SOFTWARE TO BE COPIED ONTO ANOTHER INDIVIDUAL OR LEGAL ENTITY'S COMPUTER EXCEPT AS MAY BE PERMITTED HEREIN. You may, however, transfer all your rights to use the Software to another individual or legal entity provided that: (a) you also transfer (i) this agreement, (ii) the serial number(s), the Software and all other software or hardware bundled, packaged or pre-installed with the Software, including all copies, upgrades, updates and prior versions, and (iii) all copies of font software converted into other formats to such individual or entity; (b) you retain no upgrades, updates or copies, including backups and copies stored on a computer; and (c) the receiving party accepts the terms and conditions of this agreement and any other terms and conditions under which you purchased a valid licence to the Software. Notwithstanding the foregoing, you may not transfer education, pre-release, or not for resale copies of the Software. Prior to a transfer Adobe may require that you and the receiving party confirm in writing your compliance with this agreement, provide Adobe with information about yourselves, and register as end-users of the Software. Allow 4-6 weeks to transfer. Please visit http://www.adobe.com/support/main.html or contact Adobe's Customer Support Department for more information.
5. Updates. If the Software is an upgrade or update to a previous version of the Software, you must possess a valid licence to such previous version in order to use such upgrade or update. All upgrades and updates are provided to you on a licence exchange basis. You agree that by using an upgrade or update you voluntarily terminate your right to use any previous version of the Software. As an exception, you may continue to use previous versions of the Software on your Computer after you use the upgrade or update but only to assist you in the transition to the upgrade or update, provided that the upgrade or update and the previous versions are installed on the same computer. Upgrades and updates may be licensed to you by Adobe with additional or different terms.
6. LIMITED WARRANTY. Except as may be otherwise provided in Section 14, Adobe warrants to the individual or entity that first purchases a licence for the Software for use on Computers pursuant to the terms of this agreement that the Software will perform substantially in accordance with the Documentation for the ninety (90) day period following receipt of the Software when used on the recommended operating system and hardware configuration. Non-substantial variation of performance from the Documentation does not establish a warranty right. This limited warranty does not apply to PATCHES, FONT SOFTWARE CONVERTED INTO OTHER FORMATS, pre-release (BETA), tryout, STARTER, evaluation, product sampler, OR NOT FOR RESALE (NFR) COPIES OF SOFTWARE, OR WEBSITES, ONLINE SERVICES OR CD SERVICES (See Section 14). All warranty claims must be made, along with proof of purchase, within such ninety (90) day period. If the Software does not perform substantially in accordance with the Documentation, the entire liability of Adobe and its affiliates and your exclusive remedy will be limited to either, at Adobefs option, replacement of the Software or refund of the licence fee you paid for the Software. THE LIMITED WARRANTY SET FORTH IN THIS SECTION GIVES YOU SPECIFIC LEGAL RIGHTS. YOU MAY HAVE ADDITIONAL RIGHTS WHICH VARY FROM JURISDICTION TO JURISDICTION. For further warranty information, please see the jurisdiction specific provisions at the end of this agreement, if any, or contact the Adobe Customer Support Department.
7. DISCLAIMER.   The provisions of Sections 7 and Section 8 will survive the termination of this agreement, howsoever caused, but this will not imply or create any continued right to use the Software after termination of this agreement.
8. LIMITATION OF LIABILITY.  THE FOREGOING LIMITATIONS AND EXCLUSIONS APPLY TO THE EXTENT PERMITTED BY APPLICABLE LAW IN YOUR JURISDICTION. ADOBEfS AGGREGATE LIABILITY AND THAT OF ITS AFFILIATES AND SUPPLIERS UNDER OR IN CONNECTION WITH THIS AGREEMENT WILL BE LIMITED TO THE AMOUNT PAID FOR THE SOFTWARE, IF ANY. THIS LIMITATION WILL APPLY EVEN IN THE EVENT OF A FUNDAMENTAL OR MATERIAL BREACH OR A BREACH OF THE FUNDAMENTAL OR MATERIAL TERMS OF THIS AGREEMENT. Nothing contained in this agreement limits Adobefs liability to you in the event of death or personal injury resulting from Adobefs negligence or for the tort of deceit (fraud). Adobe is acting on behalf of its affiliates and suppliers for the purpose of disclaiming, excluding and limiting obligations, warranties and liability, but in no other respects and for no other purpose. For further information, please see the jurisdiction specific information at the end of this agreement, if any, or contact the Adobe Customer Support Department.
9. Export Rules. You agree that the Software will not be shipped, transferred or exported into any country or used in any manner prohibited by the United States Export Administration Act or any other export laws, restrictions or regulations (collectively the "Export Laws"). In addition, if the Software is identified as an export controlled item under the Export Laws, you represent and warrant that you are not a citizen of, or located within, an embargoed or otherwise restricted nation (including without limitation Iran, Iraq, Syria, Sudan, Libya, Cuba and North Korea) and that you are not otherwise prohibited under the Export Laws from receiving the Software. All rights to use the Software are granted on condition that such rights are forfeited if you fail to comply with the terms of this agreement.
10. Governing Law. This agreement will be governed by and construed in accordance with the substantive laws in force in: (a) the State of California, if a licence to the Software is purchased when you are in the United States, Canada, or Mexico; or (b) Japan, if a licence to the Software is purchased when you are in Japan, China, Korea, or other Southeast Asian country where all official languages are written in either an ideographic script (e.g., hanzi, kanji, or hanja), and/or other script based upon or similar in structure to an ideographic script, such as hangul or kana; or (c) England, if a licence to the Software is purchased when you are in any jurisdiction not described above. The respective courts of Santa Clara County, California when California law applies, Tokyo District Court in Japan, when Japanese law applies, and the competent courts of London, England, when the law of England applies, shall each have non-exclusive jurisdiction over all disputes relating to this agreement. This agreement will not be governed by the conflict of law rules of any jurisdiction or the United Nations Convention on Contracts for the International Sale of Goods, the application of which is expressly excluded.
11. General Provisions. If any part of this agreement is found void and unenforceable, it will not affect the validity of the balance of this agreement, which will remain valid and enforceable according to its terms. This agreement will not prejudice the statutory rights of any party dealing as a consumer. For example, for consumers in New Zealand who obtain the Software for personal, domestic or household use (not business purposes), this agreement is subject to the Consumer Guarantees Act. This agreement may only be modified by a writing signed by an authorized officer of Adobe. The English version of this agreement will be the version used when interpreting or construing this agreement. This is the entire agreement between Adobe and you relating to the Software and it supersedes any prior representations, discussions, undertakings, communications or advertising relating to the Software.
12. Notice to U.S. Government End Users.
12.1 Commercial Items. The Software and Documentation are "Commercial Item(s)," as that term is defined at 48 C.F.R. Section 2.101, consisting of "Commercial Computer Software" and "Commercial Computer Software Documentation," as such terms are used in 48 C.F.R. 12.212 or 48 C.F.R. 227.7202, as applicable. Consistent with 48 C.F.R. 12.212 or 48 C.F.R. Sections 227.7202-1 through 227.7202-4, as applicable, the Commercial Computer Software and Commercial Computer Software Documentation are being licensed to U.S. Government end users (a) only as Commercial Items and (b) with only those rights as are granted to all other end users pursuant to the terms and conditions herein. Unpublished-rights reserved under the copyright laws of the United States. Adobe Systems Incorporated, 345 Park Avenue, San Jose, CA 95110-2704, USA.
12.2 U.S. Government Licensing of Adobe Technology. You agree that when licensing Adobe Software for acquisition by the U.S. Government, or any contractor therefore, you will license consistent with the policies set forth in 48 C.F.R. Section 12.212 (for civilian agencies) and 48 C.F.R. Sections 227-7202-1 and 227-7202-4 (for the Department of Defense). For U.S. Government End Users, Adobe agrees to comply with all applicable equal opportunity laws including, if appropriate, the provisions of Executive Order 11246, as amended, Section 402 of the Vietnam Era Veterans Readjustment Assistance Act of 1974 (38 USC 4212), and Section 503 of the Rehabilitation Act of 1973, as amended, and the regulations at 41 CFR Parts 60-1 through 60-60, 60-250, and 60-741. The affirmative action clause and regulations contained in the preceding sentence will be incorporated by reference in this agreement.
13. Compliance with Licences. If you are a business, company or organization, you agree that upon request from Adobe or its authorized representative you will within thirty (30) days fully document and certify that use of any and all Adobe software at the time of the request is in conformity with your valid licences from Adobe.
14. Specific Provisions and Exceptions. This section sets forth specific provisions related to certain components of the Software as well as limited exceptions to the above terms and conditions. To the extent that any provision in this section is in conflict with any other term or condition in this agreement, this section will supersede such other term or condition.
14.1 Limited Warranty for Users Residing in Germany or Austria. If you obtained the Software in Germany or Austria, and you usually reside in such country, then Section 6 does not apply; instead, Adobe warrants that the Software provides the functionalities set forth in the Documentation (the "agreed upon functionalities") for the limited warranty period following receipt of the Software when used on the recommended hardware configuration. As used in this Section, "limited warranty period" means one (1) year if you are a business user and two (2) years if you are not a business user. Non-substantial variation from the agreed upon functionalities will not and does not establish any warranty rights. This limited warranty does not apply to Software provided TO YOU free of charge, for example, UPDATES, pre-release, tryout, STARTER, product sampler AND NOT FOR RESALE (NFR) COPIES OF SOFTWARE, OR TO font software converted into other formats, WEB SITES, ONLINE SERVICES, CD SERVICES OR SOFTWARE THAT HAS BEEN ALTERED BY YOU, TO THE EXTENT SUCH ALTERATION CAUSED A DEFECT. To make a warranty claim, during the limited warranty period you must return, at our expense, the Software and proof of purchase to the location where you obtained it. If the functionalities of the Software vary substantially from the agreed upon functionalities, Adobe is entitled -- by way of re-performance and at its own discretion -- to repair or replace the Software. If this fails, you are entitled to a reduction of the purchase price (reduction) or to cancel the purchase agreement (rescission). For further warranty information, please contact the Adobe Customer Support Department.
14.2 Limitation of Liability for Users Residing in Germany and Austria.
14.2.1 If you obtained the Software in Germany or Austria, and you usually reside in such country, then Section 8 does not apply. Instead, subject to the provisions in Section 14.2.2, Adobe and its affiliates' statutory liability for damages will be limited as follows: (i) Adobe and its affiliates will be liable only up to the amount of damages as typically foreseeable at the time of entering into the purchase agreement in respect of damages caused by a slightly negligent breach of a material contractual obligation and (ii) Adobe and its affiliates will not be liable for damages caused by a slightly negligent breach of a non-material contractual obligation.
14.2.2 The aforesaid limitation of liability will not apply to any mandatory statutory liability, in particular, to liability under the German Product Liability Act, liability for assuming a specific guarantee or liability for culpably caused personal injuries.
14.2.3 You are required to take all reasonable measures to avoid and reduce damages, in particular to make back-up copies of the Software and your computer data subject to the provisions of this agreement.
14.3 Pre-release Software Additional Terms. If the Software is pre-commercial release or beta software ("Pre-release Software"), then this Section applies. The Pre-release Software is a pre-release version, does not represent final product from Adobe, and may contain bugs, errors and other problems that could cause system or other failures and data loss. Adobe may never commercially release the Pre-release Software. If you received the Pre-release Software pursuant to a separate written agreement, such as the Adobe Systems Incorporated Serial Agreement for Unreleased Products, your use of the Software is also governed by such agreement. You will return or destroy all copies of Pre-release Software upon request by Adobe or upon Adobe's commercial release of such Software.  SEE SECTIONS 7 and 8 FOR WARRANTY DISCLAIMERS AND LIABILITY LIMITATIONS WHICH GOVERN PRE-RELEASE SOFTWARE.
14.4 Tryout, Product Sampler, NFR, Additional Terms. If the Software is tryout, starter, product sampler, or NFR software ("Tryout Software"), then the following Section applies. The Tryout Software may contain limited functionality and is to be used for demonstration and evaluation purposes only and not for your commercial purposes.  SEE SECTIONS 7 and 8 FOR WARRANTY DISCLAIMERS AND LIABILITY LIMITATIONS WHICH GOVERN TRYOUT SOFTWARE.
14.5 Time Out Software. If the Software is a timeout version then it will cease operations after a designated period of time or number of launches following installation. The licence hereunder will terminate after such period or number of launches unless extended by Adobe upon your acquisition of a full retail licence. Access to any files or output created with such Software or any product associated with such Software is entirely at your own risk.
14.6 Educational Software Product. If the Software accompanying this agreement is Educational Software Product (Software manufactured and distributed for use by only Educational End Users), you are not entitled to use the Software unless you qualify in your jurisdiction as an Educational End User. Please visit http://www.adobe.com/education/purchasing to learn if you qualify. To find an Adobe Authorized Academic Reseller in your area, please visit http://www.adobe.com/store and look for the link for Buying Adobe Products Worldwide.
14.7. Font Software. If the Software includes font software --
14.7.1 You may use the font software with the Software on Computers as described in Section 2 and output the font software to any output device(s) connected to such Computer(s).
14.7.2 If the Permitted Number of Computers is five or fewer, you may download the font software to the memory (hard disk or RAM) of one output device connected to at least one of such Computers for the purpose of having the font software remain resident in such output device, and of one additional such output device for every multiple of five represented by the Permitted Number of Computers.
14.7.3 You may take a copy of the font(s) you have used for a particular file to a commercial printer or other service bureau, and such service bureau may use the font(s) to process your file, provided such service bureau has a valid licence to use that particular font software.
14.7.4 You may convert and install the font software into another format for use in other environments, subject to the following conditions: A computer on which the converted font software is used or installed will be considered as one of your Permitted Number of Computers. Use of the font software you have converted will be pursuant to all the terms and conditions of this agreement. Such converted font software may be used only for your own customary internal business or personal use and may not be distributed or transferred for any purpose, except in accordance with Section 4.4 of this agreement.
14.7.5 You may embed copies of the font software into your electronic documents for the purpose of printing and viewing the document. If the font software you are embedding is identified as "licensed for editable embedding" on Adobefs website at http://www.adobe.com/type/browser/legal/embeddingeula.html, you may also embed copies of that font software for the additional purpose of editing your electronic documents. No other embedding rights are implied or permitted under this licence.
14.8 Online Services.
14.8.1 The Software may rely upon or facilitate your access to websites maintained by Adobe or its affiliates or third parties offering goods, information, software and services ("Online Services"). Your access to and use of any website is governed by the terms, conditions, disclaimers and notices found on such site, for example, the Terms of Use located at http://www.adobe.com/misc/copyright.html. Adobe may at any time, for any reason, modify or discontinue the availability of any website and Online Services.
14.8.2 Adobe does not control, endorse or accept responsibility for websites or Online Services offered by third parties. Any dealings between you and any third party in connection with a website or Online Services, including delivery of and payment for goods and services and any other terms, conditions, warranties or representations associated with such dealings, are solely between you and such third party.
14.8.3 EXCEPT AS EXPRESSLY AGREED BY ADOBE OR ITS AFFILIATES OR A THIRD PARTY IN A SEPARATE AGREEMENT, YOUR USE OF WEBSITES AND ONLINE SERVICES IS AT YOUR OWN RISK. PLEASE SEE SECTIONS 7 AND 8 FOR WARRANTY AND LIABILITY LIMITATIONS WHICH GOVERN WEBSITES AND ONLINE SERVICES.
14.9 After Effects Professional Render Engine. If the Software includes the full version of Adobe After Effects Professional, then you may install an unlimited number of Render Engines on Computers within your Internal Network that includes at least one Computer on which the full version of the Adobe After Effects Professional software is installed. The term "Render Engine" means an installable portion of the Software that allows After Effects projects to be rendered but cannot be used to create or modify projects and does not include the complete After Effects user interface.
14.10 GoLive Co-Author. If the Software includes Adobe Creative Suite or Adobe GoLive software and includes the GoLive Co-Author software component, then as an alternative to installing and using a single copy of GoLive Co-Author software with the other Software components as permitted in Section 2.1, you may either (a) install and use the GoLive Co-Author software on a Permitted Number of your Computers on which the other Software components are not installed or (b) distribute one copy of GoLive Co-Author software to a Permitted Number of individuals or legal entities for installation and use by each such individual or entity on one Computer; provided that each such individual or entity accepts the terms and conditions of this agreement and any other terms and conditions under which you purchased a valid licence to the Software. Such individual or entity may not further distribute GoLive Co-Author software. If the Software is GoLive Co-Author software that you obtained on a standalone basis (i.e., not as a part of Adobe Creative Suite or GoLive software), then this Section does not apply.
14.11 Version Cue Software. If the Software includes Adobe Creative Suite software and the Version Cue software components, then as an alternative to installing and using a single copy of the Version Cue workspace component with the other components of the Software as permitted in Section 2.1, you may install the Version Cue workspace component on one Computer file server within your Internal Network that includes at least one computer on which Adobe Creative Suite software is installed. You may use Version Cue workspace software only for the purpose of operating a workspace accessible only by Computers on such Internal Network. No other network use is permitted, including, but not limited to enabling Internet or web hosted workgroups or services.
14.12 Certified Documents. If the Software allows you to author and validate Certified Documents, then this Section applies.
14.12.1 Certified Documents and CD Services. A "Certified Document" or "CD" is a PDF file that has been digitally signed using (a) the Software CD feature set; (b) a certificate; and (c) a "private" encryption key that corresponds to the "public" key in the certificate. Authoring of a CD requires that you obtain a certificate from an authorized CD Service Provider. "CD Service Provider" is an independent third party service vendor listed at http://www.adobe.com/security/partners_cds.html. Validation of a CD requires CD Services from the CD Service Provider that issued the certificate. "CD Services" are services provided by CD Service Providers, including but not limited to (a) certificates issued by such CD Service Provider for use with the Softwarefs CD feature set; (b) services related to issuance of certificates; and (c) other services related to certificates, including but not limited to verification services.
14.12.2 CD Service Providers. Although the Software provides CD authoring and validation features, Adobe does not supply the necessary CD Services required to use these features. Purchasing, availability and responsibility for the CD Services are between you and the CD Service Provider. Before you rely upon any CD, any digital signature applied thereto, and/or any related CD Services, you must first review and agree to the applicable Issuer Statement and this agreement. "Issuer Statement" means the terms and conditions under which each CD Service Provider offers CD Services (see the links on http://www.adobe.com/security/partners_cds.html), including for example any subscriber agreements, relying party agreements, certificate policies and practice statements, and Section 14.12 of this agreement. By validating a CD using CD Services, you acknowledge and agree that (a) the certificate used to digitally sign a CD may be revoked at the time of verification, making the digital signature on the CD appear valid when in fact it is not; (b) the security or integrity of a CD may be compromised due to an act or omission by the signer of the CD, the applicable CD Service Provider, or any other third party; and (c) you must read, understand, and be bound by the applicable Issuer Statement.
14.12.3 Warranty Disclaimer and Limitation of Liability. CD Service Providers offer CD Services solely in accordance with the applicable Issuer Statement.  SEE SECTIONS 7 AND 8 FOR WARRANTY DISCLAIMERS AND LIABILITY LIMITATIONS WHICH GOVERN CD SERVICES.
14.12.4 Indemnity. You agree to hold Adobe and any applicable CD Service Provider (except as expressly provided in its Issuer Statement) harmless from any and all liabilities, losses, actions, damages, or claims (including all reasonable expenses, costs, and attorneys fees) arising out of or relating to any use of, or reliance on, any CD Service, including, without limitation (a) reliance on an expired or revoked certificate; (b) improper verification of a certificate; (c) use of a certificate other than as permitted by any applicable Issuer Statement, this agreement or applicable law; (d) failure to exercise reasonable judgment under the circumstances in relying on the CD Services; or (e) failure to perform any of the obligations as required in an applicable Issuer Statement.
14.12.5 Third Party Beneficiaries. You agree that any CD Service Provider you utilize will be a third party beneficiary with respect to this Section of this agreement and that such CD Service Provider will have the right to enforce such provisions in its own name as if the CD Service Provider were Adobe.
If you have any questions regarding this agreement or if you wish to request any information from Adobe please use the address and contact information included with this product to contact the Adobe office serving your jurisdiction.
Adobe, After Effects, Co-Author, GoLive and Version Cue are either the registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries.
Type_WWRetailOnly_IntlEnglish_02.03.04


×
Subscribe to our Featured Magazine
Subscribe to our newsletters now and stay up-to-date with new collections, the latest lookbooks and exclusive magazines.
Submit


If you need to send custom parameters to the Authorization Server, make sure to use the original parameter name.
Changes to recommended defaults, like defaultScope
The default scope to be included with all requests. If
not provided, 'openid profile email' is used.
This can be set to null
in order to
effectively remove the default scopes.
Note: The openid
scope is
always applied regardless of this
setting.
The default audience to be used for requesting API access.
A maximum number of seconds to wait before declaring background calls to /authorize as failed for timeout Defaults to 60s.
Specify a custom cache implementation to use for token
storage and retrieval. This setting takes precedence over
cacheLocation
if they are both specified.
Read more about creating a custom cache
The location to use when storing cache data. Valid values
are memory
or localstorage
. The
default setting is memory
.
The Client ID found on your Application settings page
Your Auth0 account domain such as
'example.auth0.com'
,
'example.eu.auth0.com'
or ,
'example.mycompany.com'
(when using
custom domains)
Path in your application to redirect to when the
Authorization server returns an error. Defaults to
/
Configuration for the built-in Http Interceptor, used for automatically attaching access tokens.
The Id of an invitation to accept.
This is available from the user invitation URL that is given when participating in a user invitation flow.
The issuer to be used for validation of JWTs, optionally defaults to the domain above
The value in seconds used to account for clock skew in JWT expirations. Typically, this value is no more than a minute or two at maximum. Defaults to 60s.
Maximum allowable elapsed time (in seconds) since authentication. If the last time the user authenticated is greater than this value, the user must be reauthenticated.
The Id of an organization to log in to
This will specify an organization
parameter in
your user's login request and will add a step to validate
the org_id
claim in your user's ID Token.
The default URL where Auth0 will redirect your browser to with the authentication result. It must be added to the "Allowed Callback URLs" field in your Auth0 Application's settings. If not provided here, it should be provided in the other methods that provide authentication.
The default scope to be used on authentication requests. The defaultScope defined in the Auth0Client is included along with this scope
By default, if the page URL has code and state parameters,
the SDK will assume they are for an Auth0 application and
attempt to exchange the code for a token. In some cases the
code might be for something else (e.g. another OAuth SDK).
In these instances you can instruct the client to ignore
them by setting skipRedirectCallback
.
AuthModule.forRoot({
skipRedirectCallback: window.location.pathname === '/other-callback'
})
Note: In the above example,
/other-callback
is an existing route that will be
called by any other OAuth provider with a
code
(or error
in case when
something went wrong) and state
.
If true, refresh tokens are used to fetch new access tokens
from the Auth0 server. If false, the legacy technique of
using a hidden iframe and the
authorization_code
grant with
prompt=none
is used. The default setting is
false
.
Note: Use of refresh tokens must be enabled by an administrator on your Auth0 client application.
Configuration for the authentication service


spaceinvoices-js-sdk
Instalation
yarn
Usage
import dotenv from 'dotenv'
import { SpaceInvoices, siFilter } from './src/spaceInvoices'
dotenv.config()
let spaceInvoices = new SpaceInvoices(process.env.API_TOKEN)
spaceInvoices.account.authenticate(process.env.TEST_EMAIL, process.env.TEST_PASSWORD).then(account => {
console.log('account', account)
// Example 1: List All Organizations
account.listOrganizations().then(organizations => {
console.log('allOrganizations', organizations.length)
let organization = organizations.pop()
if (organization) {
organization.listDocuments().then((documents) => {
console.log('documents from organization', documents)
})
}
})
// Example 2: List Filtered Organizations
let filter = siFilter()
.where('locale').eq('en')
.and('supportPin').gte(10000)
account.listOrganizations(filter).then(organizations => {
console.log('filteredOrganizations', organizations.length)
})
}).catch((error) => {
console.error('Error', error)
})
Compile
yarn compile
Test
yarn test
Contribution guide
Developing spaceinvoices-js-sdk
You consider contributing changes to spaceinvoices-js-sdk – thank you! Please consider these guidelines when filing a pull request:
- Project uses standard JavaScript style guide, linter, and formatter. Use it and save yourselve some time
- Commits follow the Angular commit convention. The project uses commitizen library
- JavaScript is written using ES6 features
- Features and bug fixes should be covered by test cases
License
MIT © Robert Tajnšek


Before we start hacking away to get this server connected, we need to make sure that we have S3 buckets created and the right credentials to run a backup. Log into Amazon AWS. Once you are logged in, then proceed to click on S3 then you should be able create your Amazon S3 bucket using the on-screen prompts.
In this example, I will be using a bucket named myserver-pleskfull
, but your bucket name must be original and not conflict with any other bucket names on Amazon S3. Whatever you name this bucket, we will configure full server Plesk backups to go into this bucket. Make sure you change my code to whatever your bucket name is as you progress through the book.
***Best Practice Note***
If this Amazon account is only going to be accessed by one user, one server, only one user has root access to your server, and any Amazon buckets will be used only to store backups for this server… then you can proceed further down this post. If this is an existing account and you have multiple users, multiple servers, and/or you just want heightened security — then I would definitely configure policies and different security settings through Amazon IAM (Amazon Identity and Access Management). Configuring correct bucket access can help you prevent malicious users, software, etc. from gaining access to ALL of your buckets if you lock them down individually. Since I don’t want to go on a tangent talking about IAM, read that link for the AWS IAM Best Practices.
When you are logged into the AWS Console, click on your name and then choose security credentials.
Then choose Create New Access Key:
When you create the keys, be sure to download them — because you will NEVER see them again otherwise!
Once you have that downloaded, run aws configure
you will be prompted to enter your credential information. Here is the output of the aws configure
command:
[root@myserver ~]# aws configure
AWS Access Key ID [None]: XXXXXXXXXXXXXXXXXXXX
AWS Secret Access Key [None]: 1a2b3c4d5e6f7g8h9i10j11k12k
Default region name [None]: us-east-1
Default output format [None]:<LEAVE BLANK -- JUST PRESS ENTER>
For the default region, I chose us-east-1, but you can choose whatever region you would like by seeing the list from Amazon here. For what we are doing in the scope of this article, the regions are irrelevant to Amazon S3 but if you do any management of other AWS regions — it may be very important to set this correctly. You can also see that I left Default output format [None]: blank. This will default to JSON. You can read more about your options here.
Now that we have our authentication configured, you should now be able to test it out. Create some test files in your home folder on the server you will be backing up:
[root@myserver ~]# mkdir -p test-parent-folder/
[root@myserver ~]# touch testfolder/test1.txt
If you run ll
or ls -l
you should see your folder there. Now lets run the command to move it up to our S3 Bucket. Remember the bucket name that we chose from above is myserver-pleskfull
. The syntax for the aws
command is the following:
aws [options] <command> <subcommand> [parameters]
Using the syntax above (advanced syntax can be found here), we can now run our move command and watch its results:
[root@myserver ~]# aws s3 mv testfolder/ s3://myserver-pleskfull --recursive
move: testfolder/test1.txt to s3://myserver-pleskfull/test1.txt
Now when you check the bucket in the Amazon Console, you can see that it appears (notice how it copied just the file, not the folder):
You can also check via command line:
[root@myserver ~]# aws s3 ls s3://myserver-pleskfull-full --recursive
2014-04-03 12:13:13 0 test1.txt


Exodus 12:44
But every man's servant that is bought for money, when thou hast circumcised him, then shall he eat thereof.


Whether I am building distributed systems or creating real-time visualizations, my passion is solving hard problems and writing beautiful code from first principles.


ZAC PIZZA
Home
Order
Contact
×
Welcome to ZAC PIZZA
Pizza Type
Hawaiian Pizza
Pepperoni Pizza
Chicken Mushroom Pizza
BBQ chicken Pizza
Buffalo Pizza
Deepdish Pizza
Meat Pizza
Margherita Pizza
Crust
Stuffed - Ksh.150
Thick - Ksh.200
Crispy(Thin) - Ksh.100
Gluten Free - Ksh.200
Toppings
Mushroom
Cheese
Black-Olives
Radichio
Bacon
Number of Pizza's
Delivery(300 Ksh) or Collect?
Delivery
Collect
Size
Large
Medium
Small
Contact Details
First Name
Last Name
Email Address
Phone Number
Telephone Number
Get your Order


|
| Local Properties | Local Methods | |
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
*May not be presentMethods
| Methods defined in this Managed Object |
|---|
| configureVcha_Task, createPassiveNode_Task, createWitnessNode_Task, deployVcha_Task, destroyVcha_Task, getVchaConfig, prepareVcha_Task |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
| configSpec | VchaClusterConfigSpec |
contains the configuration for the cluster
|
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task | This method returns a Task object with which to monitor the progress of the operation. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
| passiveDeploymentSpec | PassiveNodeDeploymentSpec |
contains deployment specification for the
passive node
|
| sourceVcSpec | SourceNodeSpec |
contains specification for the source vCenter
server that is used to create Passive node
|
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task | This method returns a Task object with which to monitor the progress of the operation. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
| witnessDeploymentSpec | NodeDeploymentSpec |
contains deployment specification for the
witness node
|
| sourceVcSpec | SourceNodeSpec |
contains specification for the source vCenter
server that is used to create Witness node
|
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task | This method returns a Task object with which to monitor the progress of the operation. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
| deploymentSpec | VchaClusterDeploymentSpec |
contains the information needed to deploy and
configure a VCHA Cluster
|
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task | This method returns a Task object with which to monitor the progress of the operation. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
Return Value
| Type | Description |
|---|---|
| VchaClusterConfigInfo | Returns a data structure specifying configuration for Active, Passive and Witness node in the Cluster. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the FailoverClusterConfigurator used to make the method call. |
| networkSpec | VchaClusterNetworkSpec |
contains the information needed to prepare
a VCHA Cluster and configure networking.
|
Return Value
| Type | Description |
|---|---|
| ManagedObjectReference
to a Task | This method returns a Task object with which to monitor the progress of the operation. |
Faults
| Type | Description |
|---|---|
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
Events
| Type | |
|---|---|
| None |
| Top of page | Local Properties | Local Methods | |
| Managed Object Types | Data Object Types | All Properties | All Methods |


The other day I was looking back over some old emails, and I found a thread between myself and one of my old university tutors. I followed the thread to find my submission of a Java simulation of the Ising Model. I took a peek.
Looking over old code can be a humbling experience.
Once I had recovered from the initial shock I started to dig into the physics of the simulation. It's a simple enough simulation of the Ising Model in two dimensions. I say simple, because the model is conceptually simple to understand given a few diagrams and a couple of paragraphs. I know this because I quickly gave up trying to figure out how my simulation mapped back to a physical system and took to the internet for answers.
Amongst the excellent explanations there were a number of other simulations (mostly in Java) whose code I read. They all appeared to be very similar (including my original solution), solving the model with very imperative code. Often making optimizations with pretty large readability sacrifices. Like my original I found it very difficult to see the underly physics, or indeed the simulation algorithm being used through the implementation details.
I wondered what the same simulation would look like in Clojure(Script) with its functional roots and elegant data structures. So I has a go at rebuilding it in idiomatic (to the best of my current knowledge) ClojureScript to run within the browser.
I'm going to gloss over lots of the details, just explaining what I hope is enough to understand the code and the principles of the physical interactions.
The Ising model describes an idealised magnetic material, by modeling a series of interacting charged particles.
So picture a lattice of charged particles, called spins. Each of the spins can either point up or down. In this particular lattice our spins are in their lowest energy state when spins are aligned in the same direction (meaning we have a ferromagnet). Physical systems love there lowest energy states, so we expect that if we don't add any extra energy (heat) then the spins will all attempt to align, and thus magnetise.
Here is a lattice with some energy stored up. We can see that it is not in its lowest energy state by looking at the centre most spin, which is pointing down when all the spins closest to it are pointing up.
| ↑ | ↓ | ↓ | ↓ | ↓ |
| ↓ | ↑ | ↑ | ↑ | ↑ |
| ↑ | ↑ | ↓ | ↑ | ↑ |
| ↑ | ↑ | ↑ | ↑ | ↑ |
| ↑ | ↑ | ↑ | ↑ | ↑ |
At a low enough temperature we would expect that the lattice would, over time, align its spins increasing the overall magnetisation. If we were to add lots of energy the spins would be more likely to oppose their neighbours, and the magnetisation would decrease.
The Metropolis algorithm gives us some simple steps we can take to update our lattice, moving it from one state to another. Applying the Metropolis Algorithm should progress the lattice in a way the is consistent with its physical rules, but not so computationally exhaustive that we would never get anywhere.
To take the lattice from one state to another we will attempt to flip a single spin, reversing its direction. The algorithm gives us the following method to determine if the flip is acceptable.
The Ising model defines the equations needed for calculating the energy change and the probability distribution.
And that's about it. The simulation runs these Metropolis steps over and over at different temperatures to investigate the effect on the magnetisation.
Lets jump into the code and look at how I've tried to represent the above steps in Clojure.
(defn metropolis-step
"progress a lattice on by one metropolis step.
rand-pos: generates a random valid position
neighbours: finds positions of neighbours
temp: the current lattice temperature
lattice: a Set of the positive spins"
[rand-pos neighbours temp lattice]
(let [pos (rand-pos)
spin (if (contains? lattice pos) 1 -1)
delta (* spin (energy lattice (neighbours pos)))]
;; is the flip acceptable
(if (or (neg? delta)
(< (rand) (weighting delta temp)))
;; make the flip
(if (contains? lattice pos)
(disj lattice pos)
(conj lattice pos))
;; flip rejected
lattice)))
Step one of the Metropolis algorithm is to generate a random position within a lattice, I've done this by providing a function which knows how to generate a position for the given lattice.
I then go on to compute the delta
in energy required to flip
the randomly selected spin. I've used another function energy
in the process which calculates the magnitude of the energy change should
a spin be flipped.
The body of the function is then left to decide based on those values if
a spin should be flipped. weighting
calculates the probability
distribution based on the change in energy and the temperature.
To understand how this is different, lets contrast the above solution to an imperative solution in JavaScript (could have used Java, but trying to save some ink).
metropolisStep(){
int x = rand()
, y = rand()
int spin = lattice[x][y]
int west = cyclic(x - 1, width)
, east = cyclic(x + 1, width)
, north = cyclic(y + 1, height)
, south = cyclic(y - 1, height)
float delta = energy(spin, north, south, east, west)
if(delta < 0 || Math.rand() < prob(delta, temp)){
lattice[x][y] *= -1
}
}
Perhaps the most important decision in the code base will be how to model the lattice with Clojures data structures. All the code from that point on will be a means of updating, querying or rendering the lattice, so its impact will be evident throughout the code.
Clojures data structures are at the heart of the language, affecting much more than how you model your data. Clojure encourages you to program to abstractions, not the abstractions you invent, but the genuinely generic abstractions which the language is built on. And no abstractions in Clojure are more important than those of its data structures.
When I last attempted this problem in Java I created an array of arrays, to represent the 2 dimensions, then populated each item with a 1 or -1 to represent each spin.
This time I am going to use a Set
#{[1 3] [3 5] [2 2]}
Why a Set? If we forget all this talk of 2D for a moment, a lattice is a collection of spins, and each spin has a unique position. So if the positions are unique then they could be used as values in a Set. We also know that spins can only be positive or negative (1 or -1), which means that any spin that isn't positive must be negative. So the whole state of the lattice can be stored in a simple Set, where only the positions of either the positive or negative spins are held
By choosing to use one of the native collection types I will be in much better placed to leverage the full power of Clojures fabulous collection abstractions.
In this implementation a lattice will be represented by just its positive spins
Now I have Set for a lattice I want to update it using the Metropolis Alg. Each time a spin is flipped (or not) the state of the system progresses. Again the more I leverage the language the more I know it will reward me, so I am going to try and represent this progression of time as a Clojure abstraction, the sequence.
To create a sequence from a function I'm using iterate
.
Iterate returns a lazy sequence of a function applied over and over to
a seed
(inc 0)
;= 1
(first (iterate inc 0))
;= 0
(take 5 (iterate inc 0))
;= (0 1 2 3 4)
I just need to write my Metropolis step function to take a lattice as input and produce the next lattice state as output to partake in the above example


Pay {{ username }}
You're going to pay {{ amount }} XLM To the following user. In order to proceed sign the following transaction by clicking the button.


Retail security software
Retail security software
Home security software
Home security software
Retail security software
Retail security software
Home security software
Home security software
Retail stores in the US have expensive security personnel to monitor their stores because they don't have an option. Medium-sized and large retail stores can afford a Security Guard in their CCTV room that costs them $60,000+ a year. Small retail outlets cannot afford a CCTV room or a Security Guard to monitor it every corner of their store when suspicious activity takes place. Having a security software system that can detect suspicious human behaviour and thefts and alert them, can help in reducing the costs of the security guards and saving the additional cost of $60,000+ a year. Not to forget the saving of $30,000 by preventing shoplifting of goods.
Only 24% of house owners in the US own a home security system. People who do own these systems have no or fewer options to let the theft occur when they are out of the house. There is NO immediate response when suspicious behaviour is tracked. When you own a CCTV, it is more like making a movie than actually alerting you about the red flags.
Retail stores in the US have expensive security personnel to monitor their stores because they don't have an option. Medium-sized and large retail stores can afford a Security Guard in their CCTV room that costs them $60,000+ a year. Small retail outlets cannot afford a CCTV room or a Security Guard to monitor it every corner of their store when suspicious activity takes place. Having a security software system that can detect suspicious human behaviour and thefts and alert them, can help in reducing the costs of the security guards and saving the additional cost of $60,000+ a year. Not to forget the saving of $30,000 by preventing shoplifting of goods.
Only 24% of house owners in the US own a home security system. People who do own these systems have no or fewer options to let the theft occur when they are out of the house. There is NO immediate response when suspicious behaviour is tracked. When you own a CCTV, it is more like making a movie than actually alerting you about the red flags.


{% load staticfiles %}
GLOOP
Targets
{% if targets %} {% for Target in targets %}
{{ Target.title }}
{% endfor %} {% else %}
No targets are available.Make your first?
{% endif %}
Blog
Download
Bitbucket
Search
HOME
{{ target.title }}
Built With Bootstrap
{% for my_id in my_clusts %}
{{ forloop.counter }}
{% endfor %}
White display
Dark display
Protein off
Surface
Stick
Max
Display compounds
Remove
Min
ACTIVITY CHANGE
MOL FROM
MOL TO


{% load static wagtailcore_tags core_tags wagtailimages_tags wagtailuserbar %}
Made with love by Springload {% include "core/includes/icon.html" with icon="springload-logo-dark" class="springload" %}


To be honest, I wouldnt be the only one in saying im awfully excited to start my bootcamp, the more i start coding i begin to see the oppertunities that it can give. after working in prep i have been able to work with a few people so far and i already love the culture. I spoke to Rohan at the beginning of last year in hopes to start it then, but became hesistant and in alot of cases was worried about the money. I am really looking forward to building more relationships and furthering my knowledge :) .
as said above i look forward to expanding my boundaries in Web developement and not only this but also hoping to use what i take from here to give me a new resolve to start Robotics again.
From what ive noticed im not the only one who is new to the area of coding whether its github ,Javascript or CSS we are all approaching something new in some way and i hope to build a relationship with some students there. to others who may be already experienced i will try and work with what they have to say and ask alot of questions maybe giving them ideas they hadnt yet thought of.
Not entirely, However i did have the expectation of this being a class.. I had that view because i have only recently come from graduating college and it has been the way ive been taught for a while. I understand that it now relates to methods of independant study and a cojoin of working with others.
most definately ,The idea to work in pairs to further our own learning as individuals iwll mean that i can bounce ideas as well and appat my mind set.
"All our dreams can come true if we have the courage to pursue them." - Walt Disney As it is okay to be nervous, I believe that its better to approach something with heart so that your able to give it your all. This will be a challenging boot camp ahead and with alot of others around me we'll all be able to accomplish so much more.


FaceBook Comment Sentiment Classifier with BERT
Introduction
In this project, I will be designing a text classification model to help distinguish between three types of FaceBook comments:
- Appreciation
- Complaints
- Feedback
| message | Appreciation | Complaint | Feedback | |
|---|---|---|---|---|
| 30 | ugh | 0 | 1 | 0 |
| 931 | What are you lining your cans with? | 0 | 0 | 1 |
| 596 | me with my hershey cake :) | 1 | 0 | 0 |
| 434 | I can has free 8x10? | 0 | 0 | 1 |
The model will be trained on a dataset of 7,961 comments. The classifier will use the BERT architecture, which is a state-of-the-art Natural Language Processing model. The model will be trained on a GPU using the PyTorch framework.
The goal of this project is to build a classifier that achieves the highest predictive performance on a hidden dataset of 2,039 comments.
The model will be evaluated on the following metrics:
- Precision
- Recall
- F1 Score
The tools I will be using in this project are:
- Python
- PyTorch
- Machine Learning framework for training the model
- Transformers Library
- HuggingFace’s library for using pretrained BERT models
- Google Colab
- A Google-hosted cloud platform for running Jupyter notebooks. Useful when coding away from home, or in need of a high end GPU for deep learning
The steps I will be taking in this project are:
- Import the data from local environment into Google Colab Environment
- Preprocess the data for PyTorch
- Setup and Train the BERT Pipeline
- Evaluate the classifier on the test set
Importing the Data
Here is an overview of the libraries needed for the project:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
from tqdm.notebook import tqdm
from transformers import BertTokenizer, AdamW, BertForSequenceClassification, get_linear_schedule_with_warmup
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import BertForSequenceClassification
from google.colab import files
Before we can begin, we will need to upload the data to the Google Colab environment. The data is stored in a CSV file, so we will use the files
library to manually upload the unlabeled and labeled datasets.
# running this code in Google Colab will prompt you to upload the data
uploaded = files.upload()
# read labeled data into a pandas dataframe
df = pd.read_csv('FB_posts_labeled.txt', sep='\t')
# read unlabeled data into a pandas dataframe
df_test = pd.read_csv('FB_posts_unlabeled.txt', sep='\t')
Data Preparation
In order to train the model, we will need to:
- encode the output label columns into one integer value column (0, 1, 2).
- split the data into training and validation sets.
- We will use the
train_test_split
function from thesklearn
library to do this.
- We will use the
- tokenize the text data
- We will use the
BertTokenizer
class from thetransformers
library to do this.
- We will use the
- convert the data into PyTorch tensors
- this is the data type that the model will be trained on.
# create labels for classification
label_dict = {'Appreciation': 0, 'Complaint': 1, 'Feedback': 2}
# create label column that encodes labels based on dict values
conds = [df.Appreciation == 1, df.Complaint == 1, df.Feedback == 1]
choices = [0, 1, 2]
df['label'] = np.select(conds, choices, default=0)
# drop old columns
df.drop(columns=['Appreciation', 'Complaint', 'Feedback', 'postId'], inplace=True)
df
| message | label | |
|---|---|---|
| 2563 | Along with so many others, it’s “Goodbye Macy’s” for me until they give Trump the axe. | 1 |
| 2601 | My aunt loaded all of her Easter photos onto the CVS photo website. Is there a way to output them back to the computer? | 2 |
| 4778 | i have over 7000 fb friends and followers on twitter. i will not stop until they’ve all heard about my poor and inefficient treatment. | 1 |
# split data into training and validation sets, with stratification
X_train, X_val, y_train, y_val = train_test_split(df.index.values, df.label.values, test_size=0.15, random_state=12, stratify=df.label.values, shuffle=True)
print(f"length of training data: {len(X_train)}")
print(f"length of testing data: {len(X_val)}")
length of training data: 6766
length of testing data: 1195
# verify that the training and validation sets are stratified
pd.Series(y_train).value_counts(normalize=True)
pd.Series(y_val).value_counts(normalize=True)
53% of data is complaints, minority classes are Appreciation and Feedback
Creating Embeddings
The BERT model requires that the input data be tokenized and converted into embeddings. The BertTokenizer
class from the transformers
library will be used to do this. The BertTokenizer
class will:
- tokenize the text data
- convert the tokens into embeddings
- add special tokens to the beginning and end of the sequence
- pad the sequences to a maximum length
- create attention masks for the padded tokens
We will use ‘cased’ version of BERT in case capitalized comments contain info about sentiment (e.g. Anger)
# Download pretrained BERT tokenizer for embeddings
tokenizer = BertTokenizer.from_pretrained('bert-base-cased',
do_lower_case=False)
The longest text in our dataset is over 512 tokens long. We will set the maximum length to 512 tokens, since this is the limit for BERT and enable truncation.
# set max length to 512 tokens
max_len = 512
# tokenize and encode sequences in the training set
encoded_data_train = tokenizer.batch_encode_plus(
df[df.index.isin(X_train)].message.values,
add_special_tokens=True,
return_attention_mask=True,
pad_to_max_length=True,
max_length=max_len,
return_tensors='pt'
)
# tokenize and encode sequences in the validation set
encoded_data_val = tokenizer.batch_encode_plus(
df[df.index.isin(X_val)].message.values,
add_special_tokens=True,
return_attention_mask=True,
pad_to_max_length=True,
max_length=max_len,
return_tensors='pt'
)
# tokenize and encode sequences in the test set
encoded_data_test = tokenizer.batch_encode_plus(
df_test.message.values,
add_special_tokens=True,
return_attention_mask=True,
pad_to_max_length=True,
max_length=max_len,
return_tensors='pt'
)
Creating PyTorch Data Tensors
We will use the TensorDataset
class from the torch.utils.data
library to create the PyTorch data tensors. The TensorDataset
class will:
- convert the input ids, attention masks, and labels into PyTorch tensors
- create a dataset from the tensors
# convert the training set into a TensorDataset
input_ids_train = encoded_data_train['input_ids']
attention_masks_train = encoded_data_train['attention_mask']
labels_train = torch.tensor(df[df.index.isin(X_train)].label.values)
dataset_train = TensorDataset(input_ids_train, attention_masks_train, labels_train)
# convert the validation set into a TensorDataset
input_ids_val = encoded_data_val['input_ids']
attention_masks_val = encoded_data_val['attention_mask']
labels_val = torch.tensor(df[df.index.isin(X_val)].label.values)
dataset_val = TensorDataset(input_ids_val, attention_masks_val, labels_val)
# convert the test set into a TensorDataset
input_ids_test = encoded_data_test['input_ids']
attention_masks_test = encoded_data_test['attention_mask']
dataset_test = TensorDataset(input_ids_test, attention_masks_test)
Creating Data Loaders
We will use the DataLoader
class from the torch.utils.data
library to create the data loaders. The DataLoader
class will:
- shuffle the data
- create batches of data
- load the data into the GPU
# create the DataLoaders for our training and validation sets
# set batch size to 8
batch_size = 8
dataloader_train = DataLoader(dataset_train,
shuffle=True,
batch_size=batch_size)
dataloader_validation = DataLoader(dataset_val,
shuffle=True,
batch_size=batch_size)
dataloader_test = DataLoader(dataset_test,
shuffle=False,
batch_size=batch_size)
Creating the BERT Classifier
We will use the BertForSequenceClassification
class from the transformers
library to create the BERT classifier. The BertForSequenceClassification
class will:
- load the pretrained BERT model
- add a dropout layer
- add a dense layer
- add a softmax activation function
# load the pretrained BERT model
bert = BertForSequenceClassification.from_pretrained('bert-base-cased',
num_labels=len(label_dict),
output_attentions=False,
output_hidden_states=False)
Creating the Optimizer
We will use the AdamW
class from the transformers
library to create the optimizer. The AdamW
class will:
- create the Adam optimizer
- apply weight decay to the parameters
# create the optimizer
optimizer = AdamW(bert.parameters(),
lr=1e-5,
eps=1e-8)
Creating the Learning Rate Scheduler
We will use the get_linear_schedule_with_warmup
function from the transformers
library to create the learning rate scheduler. The get_linear_schedule_with_warmup
function will:
- create the learning rate scheduler
- set the initial learning rate
- set the number of warmup steps
- set the number of training steps
# get the number of training epochs
epochs = 4
# total number of training steps is number of batches * number of epochs
total_steps = len(dataloader_train) * epochs
# create the learning rate scheduler
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=0,
num_training_steps=total_steps)
Defining the Performance Metrics
We will use the accuracy_score
function from the sklearn.metrics
library to calculate the accuracy. We will also use the f1_score
function from the sklearn.metrics
library to calculate the F1 score.
# function to calculate the accuracy of our predictions vs labels
def accuracy_per_class(preds, labels):
label_dict_inverse = {v: k for k, v in label_dict.items()}
preds_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
for label in np.unique(labels_flat):
y_preds = preds_flat[labels_flat==label]
y_true = labels_flat[labels_flat==label]
print(f'Class: {label_dict_inverse[label]}')
print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\n')
# function to calculate the F1 score of our predictions vs labels
def f1_score_func(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return f1_score(labels_flat, pred_flat, average='weighted')
Evaluating the Model
We will define an evaluate
function to evaluate the model on the validation set. The evaluate
function will:
- put the model into evaluation mode
- initialize the loss and accuracy for this epoch
- iterate over the batches of the validation set
- load the batch into the GPU
- perform a forward pass
- calculate the loss
- update the loss and accuracy
# function to train the model
def evaluate(dataloader_val):
model.eval()
loss_val_total = 0
# empty list to save model predictions
predictions, true_vals = [], []
# iterate over batches
for batch in dataloader_val:
# load batch to GPU
batch = tuple(b.to(device) for b in batch)
# unpack the inputs from our dataloader
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[2],
}
# get model predictions for the current batch
with torch.no_grad():
outputs = model(**inputs)
# get the loss of the model predictions for the current batch
loss = outputs[0]
logits = outputs[1]
# add on to the total loss
loss_val_total += loss.item()
# model predictions are stored on GPU. So, push it to CPU
logits = logits.detach().cpu().numpy()
label_ids = inputs['labels'].cpu().numpy()
# append the model predictions and true values
predictions.append(logits)
true_vals.append(label_ids)
# compute the training loss of the epoch
loss_val_avg = loss_val_total/len(dataloader_val)
predictions = np.concatenate(predictions, axis=0)
true_vals = np.concatenate(true_vals, axis=0)
return loss_val_avg, predictions, true_vals
Before we train the model, we will set seeds for reproducibility and verify that the GPU is available.
# set the seed for reproducibility
seed_val = 17
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# check if GPU is available
if torch.cuda.is_available():
# tell PyTorch to use the GPU
device = torch.device('cuda')
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device('cpu')
# push the model to GPU
model.to(device)
Defining the Training Loop
We will use the train
function to define the training loop. The train
function will:
- put the model into training mode
- initialize the loss and accuracy for this epoch
- iterate over the batches of the training set
- load the batch into the GPU
- clear the gradients
- perform a forward pass
- calculate the loss
- perform a backward pass
- clip the gradients to 1
- update the weights
- update the learning rate
- update the loss and accuracy
# function to train the model
for epoch in tqdm(range(epochs)):
# perform one full pass over the training set
model.train()
# initialize the loss and accuracy for this epoch
loss_train_total = 0
progress_bar = tqdm(dataloader_train,
desc='Epoch {:1d}'.format(epoch),
leave=False,
disable=False)
# iterate over batches
for batch in progress_bar:
# clear previously calculated gradients
model.zero_grad()
# load batch to GPU
batch = tuple(b.to(device) for b in batch)
# unpack the inputs from our dataloader
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'labels': batch[2],
}
# get model predictions for the current batch
outputs = model(**inputs)
# get the loss of the model predictions for the current batch
loss = outputs[0]
# add on to the total loss
loss_train_total += loss.item()
# backward pass to calculate the gradients
loss.backward()
# clip the the gradients to 1.0. It helps in preventing the exploding gradient problem
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# update parameters
optimizer.step()
# update the learning rate
scheduler.step()
# update progress bar
progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})
#save the mdoel at each epoch
torch.save(model.state_dict(), f'finetuned_BERT_epoch_{epoch}.model')
# compute the training loss of the epoch
loss_train_avg = loss_train_total/len(dataloader_train)
# print epoch # and training loss
tqdm.write(f'\nEpoch {epoch}')
tqdm.write(f'Training loss: {loss_train_avg}')
# compute the validation loss & accuracy of the epoch
val_loss, predictions, true_vals = evaluate(dataloader_val)
val_f1 = f1_score_func(predictions, true_vals)
# print validation loss & accuracy
tqdm.write(f'Validation loss: {val_loss}')
tqdm.write(f'F1 Score (Weighted): {val_f1}')
Epoch 1
Training loss: 0.5482631898960384
Validation loss: 0.5348240941578235
F1 Score (Weighted): 0.8805902422136439
Epoch 2
Training loss: 0.3173633142948431
Validation loss: 0.5934633307977647
F1 Score (Weighted): 0.885306253267006
Epoch 3
Training loss: 0.16964389977592292
Validation loss: 0.69995356636486
F1 Score (Weighted): 0.8886812897554138
Epoch 4
Training loss: 0.09559309570186539
Validation loss: 0.7222233983969576
F1 Score (Weighted): 0.8836152107516756
Validation on Test Data
Before validating on our unlabeled test data, we need to import the best model weights. We will use the model weights from epoch 3 since it achieved the best F1 score.
# Load in the model weights
model.load_state_dict(torch.load('finetuned_BERT_epoch_4.model',
map_location=torch.device('cuda')))
Next, we can use the saved model to get the predictions on the test set.
# evaluate saved model on testing dataset
model.eval()
predictions = []
for batch in dataloader_test:
batch = tuple(b.to(device) for b in batch)
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs[0]
logits = logits.detach().cpu().numpy()
predictions.append(logits)
predictions = np.concatenate(predictions, axis=0)
# convert probabilities to class labels and flatten the list
predictions = np.argmax(predictions, axis=1).flatten()
Finally, we can create a submission file with the predictions.
# append label predictions to test dataframe
df_test['pred'] = predictions
# convert label predictions to original labeled columns
df_test = pd.get_dummies(df_test, columns=['pred'])
df_test.columns = ['postId', 'message', 'Appreciation_pred', 'Complaint_pred', 'Feedback_pred']
del df_test['message']
# save predictions to csv file
df_test.to_csv('submission.csv', index=False)
Conclusion
Without any hyperparameter tuning, we were able to achieve an F1 score of 0.88 on the test set. This is a great result considering that we only used 10% of the training data. We can further improve the model by using more training data and tuning the hyperparameters. We can also try other models such as XLNet and RoBERTa.


Choose the date the accounts are made up to
A company can file its accounts up to 7 days before or 7 days after its accounting period date. This will not change the end date of its future accounting periods.


Brokers
Create Broker
Code
Name
Broker One
×
Manage Broker
code:
name:
TRN:
Global Name Id:
Source Id:
Broker One:
False
True
Logo
Motor Vehicle Special Rates
Name
Value
Add
Home Special Rates
Name
Value
Add
Branches
Add Branch
Delete Branch


This is a Ge’ez Berhan mode (ዘግዕዝ-ብርሃን ቅጽ ተራ-ፊደል) that works as a computer-keyboard input. It requires a font supporting the Unicode 4.1 standard, “AbyssinicaSIL” is recommended and provided with this package. This Ge’ez Berhan list mode is designed for the US English QWERTY (ቅወርትይ) keyboard. This means that the acting keyboard requires to be in the US English keyboard-mode according to the US standard model.
Documentation for this keyboard is available here: GeezBerhan-Manual.pdf
Copyright © 2021 Fr. Berhanemeskel M. Embaye OFM Cap.
The MIT License (MIT)Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |


Large Golden Ratio
Large Golden Ratio
Small Golden Ratio
Like you, I used to think the world was this great place where everybody lived by the same standards I did, then some kid with a nail showed me I was living in his world, a world where chaos rules, not order.
Samuel L. Lipsum
Large Golden Ratio
Small Golden Ratio
Lorem Ipsum dolor sit amet sic transit gloria mundi, 100% width and now for a horizontal rule
Sic transit gloria mundi.
50%, vertically centered to its parent
Full Width Non Grid
Golden Ratio Small
Mauris pulvinar ac est ac lacinia. Sed tincidunt lectus ac magna adipiscing, eu placerat nulla vehicula. In id lectus elit. Aenean et mauris non lorem luctus gravida non non dolor. Aliquam ultrices enim ac velit vulputate.
Mauris pulvinar ac est ac lacinia. Sed tincidunt lectus ac magna adipiscing, eu placerat nulla vehicula. In id lectus elit. Aenean et mauris non lorem luctus gravida non non dolor. Aliquam ultrices enim ac velit vulputate.
33.3%
66.6%
33.3%
33.3%
33.3%
33.3% pushed by 66.6%
Full of text and other goodness, the content must excite, surely!
25%
75%
1. 25%
2. 25%
3. 25%
4. 25%
2. 25%, pushed by 25%
4. 25%, pushed by another 25%
4:3 ratio responsive video demo, small enough to show that it only scales up to 100% width set in HTML, but scales down smoothly
...and for 16:9 ratio:


<dependency>
<groupId>com.tinkerpop.blueprints</groupId>
<artifactId>blueprints-rexster-graph</artifactId>
<version>??</version>
</dependency>
Rexster is a RESTful graph shell that exposes any Blueprints graph through a standalone HTTP server. RexsterGraph
talks to a Rexster-exposed graph via the Rexster RESTful API. Thus, RexsterGraph
can be used to communicate with a remote graph with complete HTTP-transparency to the user. RexsterGraph
is composed of HTTP calls (through java.net.*
) and JSON parsing.
Graph graph = new RexsterGraph("http://localhost:8182/graphs/tinkergraph");
Note – RexsterGraph
is not meant for high-performing graph traversals. It is best suited for two things:
execute
method defined below.It is possible to execute raw Gremlin scripts through RexsterGraph
by using the execute
method. The execute
method will always return a JSONArray
of results. Using the Gremlin REPL:
gremlin> g = new RexsterGraph("http://localhost:8182/graphs/tinkergraph")
==>rexstergraph[http://localhost:8182/graphs/tinkergraph[mocktinkertransactionalgraph[vertices:6 edges:6 directory:data/graph-example-1]]]
gremlin> g.execute("g.v(1)")
==>[{"name":"marko","age":29,"_id":"1","_type":"vertex"}]
It is also possible to pass variables, as a second parameter, that will be bound to the script on execution.
gremlin> g.execute("g.v(x)",[x:1])
==>[{"name":"marko","age":29,"_id":"1","_type":"vertex"}]
gremlin> g.execute("g.v(x)",[x:1]).get(0).name
==>marko
gremlin> g.execute("g.v(x.toArray())",'{"x":[1,2]}')
==>[{"name":"marko","age":29,"_id":"1","_type":"vertex"},{"name":"vadas","age":27,"_id":"2","_type":"vertex"}]
gremlin> import org.codehaus.jettison.json.JSONObject
...
gremlin> jsonParams = new JSONObject('{"x":[1,2]}')
==>{"x":[1,2]}
gremlin> g.execute("g.v(x.toArray())",jsonParams)
==>[{"name":"marko","age":29,"_id":"1","_type":"vertex"},{"name":"vadas","age":27,"_id":"2","_type":"vertex"}]
Note that the Gremlin Extension must be configured in Rexster for this function to work.


excelenium
Suite
New
Ctrl+Y
Load
Ctrl+L
Export
Ctrl+E
New Script
Run
Errors
Save
Settings
Scripts
There are no scripts to display. Please create a script by clicking "New Script" button.
Suite Settings
×
Suite Name
Server Url
Browsers
Select the browsers to run the test and enter the driver path
Chrome
Firefox
Edge
Opera
Safari
User-Agent
Run Browsers Concurrently
New Script
×
Script Name
Stop on Error
Execute
Upload Suite
×
Choose file
Upload
Errors / Warnings
×
There are no errors to display. Please save the suite to see validation errors, if any.


--- layout: narrow title: Quiz collection: games screenshot: /images/game-screenshots/quiz.png styles: style.css scripts: - //ajax.googleapis.com/ajax/libs/angularjs/1.2.20/angular.min.js - //cdnjs.cloudflare.com/ajax/libs/underscore.js/1.6.0/underscore-min.js - script.js ---
This quiz will test your knowledge of the Professor P books. Answer {{ numQuestions }} questions correctly before the time runs out.
There is a {{ penalty }} second penalty for every incorrect answer.


Bradley L. Davis
About
About
Work
Work
Labs
Labs
Life
Life
Email
Email
Science & Technology
Pullman, WA
Auburn, WA
Pullman, WA
Auburn, WA
Auburn, WA
St. Louis, MO
St. Louis, MO
Houston, TX
Houston, TX
Houston, TX
Houston, TX


silver nanoparticles essential quinquefasciatus using oil Cx. larvicidal ZEO-AgNPs mosquito AgNPs zedoaria synthesized Nil activity {border-style ZEO peak px;} #ffffff;} aegypti larvae strains Aedes Culex Res. synthesis used (.) Figure control Parasitol. absorption vector ./s--- Larvicidal oils Ae. ppm, vectors (), AgNO X-ray deltamethrin-susceptible extract natural px; water {font-family UV/VIS activities font-size larval respectively. shown solution vibration (LC (ZEO) -resistant Mater. Nanomed. Say Silver analysis aqueous color deionized double; effects indicated mortality pattern plant reduction strain stretching study toxic AgNPs. Appl. Biotechnol. Culicidae) Curcuma EDX Kumar Nanoparticles Sci. Thailand. biosynthesis biosynthesized border-color border-width dengue extracts formation green groups. larvae. peaks plants potential ppm reports resistance showed solid; spectrometry synthetic toxicity {font-style {font-weight (XRD) (ZEO-AgNPs). ./.. .°, AgNPs, Artif. Bangkok, Cells Characterization Chem. Eng. FTIR Ga’al Green Mol. Plant Synthesis XRD [], agent appearance bold courier; crystalline development diffraction em; evaluation exposure filariasis globular human investigated laboratory leaf metallic mosquitoes potency presence pt;} reducing research result sharp size spectra stability strain, strong untreated values


Description
Freight Buddy was created to help people (requesters) gain access to their beloved overseas products by enlisting the help of others overseas to purchase the products and ship them over to the requesters.
Motivation
Ever once in a while, there will be a product sold overseas that my friends and I want but can't be found on any e-commerce platform, or the shop that sells the product does not ship to my country. Thus sprouted the idea of a peer-to-peer platform which could solve this problem, where people finally have an avenue for getting their hands on the product they dearly want.
Technologies Used
React, AJAX(Axios), Bootstrap, Webpack
PostgreSQL/Sequelize, Node/Express
GitHub, Heroku, AWS S3
Main Features
- A user (requester) can create/cancel a request for a product.
- A user (potential helper) can offer help by accepting a request.
- A user can update the status of a request (eg. if the helper has bought the product or it has already been shipped and awaiting confirmation of receipt from the requester)
ERD
Main Learning Points
- Integrated Webpack into React to pre-process and compile JS and CSS files to reduce file processing time.
- Implemented React hooks to manage states.
- Implemented Sequelize for ORM database querying to improve code robustness by reducing human errors.
- Implemented Multer npm package to enable multi-file uploads of listing images to AWS S3.
- Implemented Bootstrap cards and carousel components.
- Architected application according to Model-View-Controller pattern to follow code organisation best practices.