Krishna Gopal Singh is seeking a role implementing, developing, customizing, and providing support for BMC ADDM and Remedy ITSM applications. He has 2 years of experience with BMC ADDM and Remedy ARS and ITSM. He is proficient in BMC ADDM application implementation, development, customization, and support. He has experience with discovery tasks, integration with CMDB, and customizing templates in ADDM.
Ajay Purushothaman provides a curriculum vitae summarizing his experience as a Sr. System Executive with over 2 years of experience in auto discovery and application modeling using tools like BMC ADDM, BMC CMDB, and BMC BladeLogic Client Automation. He completed an ITIL Foundation certification and was recognized with an internal award in 2015. He holds a B.E. from ANNA University and has technical skills in tools like BMC ADDM, Oracle, and Microsoft SQL. His project experience includes roles as an ADDM consultant for PayPal and as an auto discovery lead for Cognizant where he configured and managed BMC ADDM for discovery, application modeling, and data syncing to CMDB.
This document discusses extending ADDM discovery capabilities to include network devices like routers, load balancers, and firewalls. Currently, ADDM is good at mapping application and software dependencies but not network neighbors. The document provides examples of how a multinational retailer and investment bank mapped load balancers and firewalls into their ADDM models to improve visibility of application connectivity. Traceroute scripts were used to map firewall connections when direct discovery methods were restricted. Extending ADDM to include network devices provides a holistic view of "what is connected where" across datacenters.
Update CMDB Using Discovery Topology (BMC ADDM) Vyom Labs
Atrium Discovery and Dependency Mapping automatically discovers physical and virtual IT assets, applications, and the relationship between them. Learn how to keep CMDB updated.
A Configuration Management Database (CMDB) is a central repository that contains information about all the components of an IT system. It allows an IT manager like Nitesh to have visibility into what servers exist, what applications they host, and how they relate to each other. The document discusses planning a CMDB by starting small and identifying existing sources of component information. It emphasizes following ITIL best practices for implementation, including selecting components to track, defining change control processes, and verifying the accuracy of records. Maintaining a CMDB provides business value by supporting services and users.
The document discusses appliance baseline monitoring in Atrium Discovery, which establishes a baseline configuration when an appliance is installed or changed and checks for differences on restart or manually. It describes viewing baseline status and differences, controlling baseline actions like updating or checking the baseline, and configuring baseline options such as severity levels, actions, and allowed network services.
This document summarizes the appliance snapshot feature in Tideway Foundation. It allows taking snapshots of datastores and configuration to clone VMs for testing or disaster recovery. Snapshots store only data and critical configs, expecting to be restored on the same Foundation release. The steps outlined include putting the appliance in maintenance mode, stopping discovery, creating and downloading snapshots, and restoring snapshots. Post-restore steps include exiting maintenance mode and starting discovery. Settings allow configuring snapshot disk thresholds and migration timeouts.
The document discusses monitoring Discovery in Tideway in three main aspects: per run, per credential/slave, and current state. It provides details on viewing reports for individual discovery runs, credential/slave success rates, and the Discovery Dashboard for current access levels. Key metrics discussed include discovery run summaries, endpoint access analysis, credential success rates shown in different colors, and the Discovery Radar for classifying last access to IPs.
Krishna Gopal Singh is seeking a role implementing, developing, customizing, and providing support for BMC ADDM and Remedy ITSM applications. He has 2 years of experience with BMC ADDM and Remedy ARS and ITSM. He is proficient in BMC ADDM application implementation, development, customization, and support. He has experience with discovery tasks, integration with CMDB, and customizing templates in ADDM.
Ajay Purushothaman provides a curriculum vitae summarizing his experience as a Sr. System Executive with over 2 years of experience in auto discovery and application modeling using tools like BMC ADDM, BMC CMDB, and BMC BladeLogic Client Automation. He completed an ITIL Foundation certification and was recognized with an internal award in 2015. He holds a B.E. from ANNA University and has technical skills in tools like BMC ADDM, Oracle, and Microsoft SQL. His project experience includes roles as an ADDM consultant for PayPal and as an auto discovery lead for Cognizant where he configured and managed BMC ADDM for discovery, application modeling, and data syncing to CMDB.
This document discusses extending ADDM discovery capabilities to include network devices like routers, load balancers, and firewalls. Currently, ADDM is good at mapping application and software dependencies but not network neighbors. The document provides examples of how a multinational retailer and investment bank mapped load balancers and firewalls into their ADDM models to improve visibility of application connectivity. Traceroute scripts were used to map firewall connections when direct discovery methods were restricted. Extending ADDM to include network devices provides a holistic view of "what is connected where" across datacenters.
Update CMDB Using Discovery Topology (BMC ADDM) Vyom Labs
Atrium Discovery and Dependency Mapping automatically discovers physical and virtual IT assets, applications, and the relationship between them. Learn how to keep CMDB updated.
A Configuration Management Database (CMDB) is a central repository that contains information about all the components of an IT system. It allows an IT manager like Nitesh to have visibility into what servers exist, what applications they host, and how they relate to each other. The document discusses planning a CMDB by starting small and identifying existing sources of component information. It emphasizes following ITIL best practices for implementation, including selecting components to track, defining change control processes, and verifying the accuracy of records. Maintaining a CMDB provides business value by supporting services and users.
The document discusses appliance baseline monitoring in Atrium Discovery, which establishes a baseline configuration when an appliance is installed or changed and checks for differences on restart or manually. It describes viewing baseline status and differences, controlling baseline actions like updating or checking the baseline, and configuring baseline options such as severity levels, actions, and allowed network services.
This document summarizes the appliance snapshot feature in Tideway Foundation. It allows taking snapshots of datastores and configuration to clone VMs for testing or disaster recovery. Snapshots store only data and critical configs, expecting to be restored on the same Foundation release. The steps outlined include putting the appliance in maintenance mode, stopping discovery, creating and downloading snapshots, and restoring snapshots. Post-restore steps include exiting maintenance mode and starting discovery. Settings allow configuring snapshot disk thresholds and migration timeouts.
The document discusses monitoring Discovery in Tideway in three main aspects: per run, per credential/slave, and current state. It provides details on viewing reports for individual discovery runs, credential/slave success rates, and the Discovery Dashboard for current access levels. Key metrics discussed include discovery run summaries, endpoint access analysis, credential success rates shown in different colors, and the Discovery Radar for classifying last access to IPs.
The document discusses troubleshooting discovery by understanding the Discovery Access page, which provides a summary of discovery sessions and results. It describes how to interpret the data on the page to identify issues and locate scripts that failed. The document also recommends specific reports and techniques for instrumenting scripts that can help troubleshoot common problems with discovery.
The document discusses discovery scripts used by Atrium Discovery to gather information from devices. It explains that scripts are organized by platform and method, and may require different access types. It provides examples of Unix and Windows discovery scripts, noting Windows scripts typically use multiple access types like WMI and require a Windows slave host.
The document provides guidance on configuring credentials for Atrium Discovery to allow it to access and discover environments. It describes storing credentials in an encrypted vault, adding different types of credentials like UNIX, Windows, SNMP and database credentials, and best practices for credential ordering.
The document describes the discovery process used by Atrium Discovery to scan devices on a network. It involves the following key steps:
1. Scanning IPs to determine accessibility and detect open ports. Credentials are used to try accessing devices.
2. Classifying devices and collecting additional information if a host is detected. Cached credentials are used for faster future access.
3. Optimization is done to avoid rescanning the same hosts multiple times and minimize network load.
4. Discovery is restricted for sensitive devices and full discovery only occurs if required information can be retrieved from hosts.
The document describes the discovery process used by Atrium Discovery to scan devices on a network. It involves the following key steps:
1. Scanning IPs to determine accessibility and detect open ports. Credentials are used to try accessing devices.
2. Classifying devices and collecting additional information if a host is detected. Cached credentials are used for faster future access.
3. Optimization is done to avoid rescanning the same hosts multiple times. Duplicated scans are skipped.
4. Discovery is restricted for sensitive devices and full discovery only occurs if required information can be collected from host devices.
The document discusses the basics of scanning networks using Atrium Discovery, including specifying what to scan, scheduling scans, choosing scanning levels, and viewing results. It covers defining IP ranges and credentials, running discovery scans to gather host and system information, and accessing detailed reports on individual discovery runs and detected nodes.
The document provides an introduction to patterns in Atrium Discovery. It explains that patterns are used to infer additional information during the discovery process based on data collected. New patterns are added through regular Knowledge Updates and custom patterns can be written. Patterns contain triggers that are checked for during discovery and can perform additional commands. Pattern provenance is tracked to show the source of all inferred data.
The document discusses how to use the query builder tool in Tideway to customize list views and save queries. It provides details on the layout and tabs of the query builder, how to add and evaluate conditions on attributes, customize columns, and save queries for future use. A walkthrough demonstrates filtering the host list by vendor and hostname, customizing columns, and saving the query.
This document provides an overview of the query language for searching and extracting data from a datastore. It describes basic keywords like SEARCH and WHERE for formulating queries, and SHOW for controlling the display of results. Examples are given for simple and advanced query conditions, regular expressions, and exercises for putting the concepts together into complete queries.
The document discusses the user interface of a reporting tool and how it allows users to access and analyze data. It describes the various views, search and filtering options, and contextual analysis reports and visualizations that are driven by the user's location and selections in the UI. These include list views, node views, chart views, and advanced search capabilities to help users find relevant information and turn data into useful insights.
This document summarizes the host grouping and visualization tools in Atrium Discovery. It describes how to use automatic grouping to organize hosts, create manual groups, and export host profiles. Key features covered include automatic grouping, manual grouping, the visualization tool, and creating host profile reports.
This document summarizes dashboards in Tideway, including default dashboards, changing dashboards, channel types (charts, summaries, videos, RSS), editing dashboards, dashboards at the CLI, and channel edits in the UI. Key points include default dashboards for new users, changing dashboards by selecting from a dropdown menu, different channel types for presenting data, copying and editing dashboards, and administrative tools for managing dashboards at the CLI and previewing channel edits in the UI.
The document discusses Atrium Discovery's taxonomy and data model. The taxonomy defines the structure of data stored in the datastore and guides how the UI displays it. The taxonomy determines what nodes and relationships can be created. It also defines expected attributes. The taxonomy and datastore together form Atrium Discovery's overall data model. The document provides instructions for viewing the taxonomy to examine node kinds, attributes, and relationships.
This document summarizes user administration and system security features in Tideway Foundation. It discusses user management including adding and managing users and groups. It also covers LDAP integration for authentication, configuring security policies like login pages and auditing, and managing active sessions and audit logs. System level accounts on the CLI are also mentioned.
This document provides instructions for installing a BMC Atrium Discovery virtual appliance in 3 steps:
1) Downloading and installing VMware Server 2.0 to run the virtual appliance.
2) Obtaining the virtual appliance file and expanding it.
3) Adding the virtual appliance to the VMware Server inventory and powering it on.
The document outlines the prerequisites and requirements for using Atrium Discovery, including supported platforms, resources needed, credentials required for scanning Unix and Windows systems, firewall configuration options, and best practices for planning scanning and involving stakeholders. Discovery works by running commands on target systems to collect configuration data and supports various access methods including SSH, WMI, SNMP, and administrative shares. Proper credentials, remote access, and installed tools are needed depending on the operating system and discovery method used.
Atrium Discovery software operates by using credential slaves to log into hosts on a Windows network using supplied credentials and delegate discovery. It stores directly discovered data in a datastore and uses a reasoning platform to apply patterns to the data to infer additional information like the presence of hosts, applications, and business applications. Patterns can be updated monthly and customized by the user. All information is indexed and searchable through interactive dashboards, reports, and search tools.
The document discusses the challenges of understanding complex IT environments and outlines the requirements of an effective baseline process. It then introduces BMC Atrium Discovery as a solution that is agent-free, extensible, low impact, and scalable. Atrium Discovery provides automatic discovery of IT assets with monthly knowledge updates and graph-based mapping of configuration items and their relationships.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
The document discusses troubleshooting discovery by understanding the Discovery Access page, which provides a summary of discovery sessions and results. It describes how to interpret the data on the page to identify issues and locate scripts that failed. The document also recommends specific reports and techniques for instrumenting scripts that can help troubleshoot common problems with discovery.
The document discusses discovery scripts used by Atrium Discovery to gather information from devices. It explains that scripts are organized by platform and method, and may require different access types. It provides examples of Unix and Windows discovery scripts, noting Windows scripts typically use multiple access types like WMI and require a Windows slave host.
The document provides guidance on configuring credentials for Atrium Discovery to allow it to access and discover environments. It describes storing credentials in an encrypted vault, adding different types of credentials like UNIX, Windows, SNMP and database credentials, and best practices for credential ordering.
The document describes the discovery process used by Atrium Discovery to scan devices on a network. It involves the following key steps:
1. Scanning IPs to determine accessibility and detect open ports. Credentials are used to try accessing devices.
2. Classifying devices and collecting additional information if a host is detected. Cached credentials are used for faster future access.
3. Optimization is done to avoid rescanning the same hosts multiple times and minimize network load.
4. Discovery is restricted for sensitive devices and full discovery only occurs if required information can be retrieved from hosts.
The document describes the discovery process used by Atrium Discovery to scan devices on a network. It involves the following key steps:
1. Scanning IPs to determine accessibility and detect open ports. Credentials are used to try accessing devices.
2. Classifying devices and collecting additional information if a host is detected. Cached credentials are used for faster future access.
3. Optimization is done to avoid rescanning the same hosts multiple times. Duplicated scans are skipped.
4. Discovery is restricted for sensitive devices and full discovery only occurs if required information can be collected from host devices.
The document discusses the basics of scanning networks using Atrium Discovery, including specifying what to scan, scheduling scans, choosing scanning levels, and viewing results. It covers defining IP ranges and credentials, running discovery scans to gather host and system information, and accessing detailed reports on individual discovery runs and detected nodes.
The document provides an introduction to patterns in Atrium Discovery. It explains that patterns are used to infer additional information during the discovery process based on data collected. New patterns are added through regular Knowledge Updates and custom patterns can be written. Patterns contain triggers that are checked for during discovery and can perform additional commands. Pattern provenance is tracked to show the source of all inferred data.
The document discusses how to use the query builder tool in Tideway to customize list views and save queries. It provides details on the layout and tabs of the query builder, how to add and evaluate conditions on attributes, customize columns, and save queries for future use. A walkthrough demonstrates filtering the host list by vendor and hostname, customizing columns, and saving the query.
This document provides an overview of the query language for searching and extracting data from a datastore. It describes basic keywords like SEARCH and WHERE for formulating queries, and SHOW for controlling the display of results. Examples are given for simple and advanced query conditions, regular expressions, and exercises for putting the concepts together into complete queries.
The document discusses the user interface of a reporting tool and how it allows users to access and analyze data. It describes the various views, search and filtering options, and contextual analysis reports and visualizations that are driven by the user's location and selections in the UI. These include list views, node views, chart views, and advanced search capabilities to help users find relevant information and turn data into useful insights.
This document summarizes the host grouping and visualization tools in Atrium Discovery. It describes how to use automatic grouping to organize hosts, create manual groups, and export host profiles. Key features covered include automatic grouping, manual grouping, the visualization tool, and creating host profile reports.
This document summarizes dashboards in Tideway, including default dashboards, changing dashboards, channel types (charts, summaries, videos, RSS), editing dashboards, dashboards at the CLI, and channel edits in the UI. Key points include default dashboards for new users, changing dashboards by selecting from a dropdown menu, different channel types for presenting data, copying and editing dashboards, and administrative tools for managing dashboards at the CLI and previewing channel edits in the UI.
The document discusses Atrium Discovery's taxonomy and data model. The taxonomy defines the structure of data stored in the datastore and guides how the UI displays it. The taxonomy determines what nodes and relationships can be created. It also defines expected attributes. The taxonomy and datastore together form Atrium Discovery's overall data model. The document provides instructions for viewing the taxonomy to examine node kinds, attributes, and relationships.
This document summarizes user administration and system security features in Tideway Foundation. It discusses user management including adding and managing users and groups. It also covers LDAP integration for authentication, configuring security policies like login pages and auditing, and managing active sessions and audit logs. System level accounts on the CLI are also mentioned.
This document provides instructions for installing a BMC Atrium Discovery virtual appliance in 3 steps:
1) Downloading and installing VMware Server 2.0 to run the virtual appliance.
2) Obtaining the virtual appliance file and expanding it.
3) Adding the virtual appliance to the VMware Server inventory and powering it on.
The document outlines the prerequisites and requirements for using Atrium Discovery, including supported platforms, resources needed, credentials required for scanning Unix and Windows systems, firewall configuration options, and best practices for planning scanning and involving stakeholders. Discovery works by running commands on target systems to collect configuration data and supports various access methods including SSH, WMI, SNMP, and administrative shares. Proper credentials, remote access, and installed tools are needed depending on the operating system and discovery method used.
Atrium Discovery software operates by using credential slaves to log into hosts on a Windows network using supplied credentials and delegate discovery. It stores directly discovered data in a datastore and uses a reasoning platform to apply patterns to the data to infer additional information like the presence of hosts, applications, and business applications. Patterns can be updated monthly and customized by the user. All information is indexed and searchable through interactive dashboards, reports, and search tools.
The document discusses the challenges of understanding complex IT environments and outlines the requirements of an effective baseline process. It then introduces BMC Atrium Discovery as a solution that is agent-free, extensible, low impact, and scalable. Atrium Discovery provides automatic discovery of IT assets with monthly knowledge updates and graph-based mapping of configuration items and their relationships.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
As this new version of the product is still being integrated into the larger BMC suite of products, you should not use it to populate the Atrium CMDB if it is also being populated by other BMC products such as BMC Bladelogic or BMC BladeLogic Client Automation (formerly BMC Configuration Automation for Clients). This would result in duplicate computer system CIs being inserted into the BMC Asset dataset. If this situation applies to you, please continue to use ADDM 7.5.01. BMC is currently working to provide full compatibility with these products and plans to release an update shortly.
You can download the ADDM Integration Extension from the CMDB Sync page. You must install the extension before you can export data to BMC Atrium CMDB. See Exporting to BMC Atrium CMDB for more information on the configuration steps required.
You can download the ADDM Integration Extension from the CMDB Sync page. You must install the extension before you can export data to BMC Atrium CMDB. See Exporting to BMC Atrium CMDB for more information on the configuration steps required.
If you choose the Custom option, you can edit the cron file to specify the exact timing of the synchronization. Edit the $TIDEWAY/etc/cron/tw_exporter_CMDB-Sync.cron file. After editing the tw_exporter_CMDB-Sync.cron file you must run tw_cron_update to apply the change.