The document evaluates caching strategies, including the standard Least Recently Used (LRU) strategy. It finds that LRU has deficits in cache hit rate compared to optimal strategies, especially for small caches. Statistics-based strategies that consider request frequency over a sliding window or with geometric weighting of past requests can converge to the optimal hit rate for static popularity distributions. Simulations confirm these strategies outperform LRU for realistic workloads.
Spark for Behavioral Analytics Research: Spark Summit East talk by John W uSpark Summit
This presentation reports our experience on using the machine learning techniques in Apache Spark ecosystem to understand the user behavior in a number of applications. In this context, Spark makes the vast computing power of a large high-performance computing system available to the behavioral economists without requiring the application scientists to learn about parallel computing. To illustrate the effectiveness of this approach, we focus on a compute-intensive task of establishing baseline for studying the impact of policies on consumer behavior. The gold standard for this type of baseline is a randomized control group, however, this control group can only provide a group-level reference, not for individual consumers. In many cases, the self-selection bias along with other factors can make it extremely difficult to generate a unbiased control group. By harnessing the computing power of Spark, we are able to learn the behavior pattern for each individual user and therefore create a much more precise baseline for behavioral analysis. We will use two use cases to illustrate the approach: a residential electricity usage study and a traffic pattern prediction study.
What is a real-time recommendation engine? Our Senior Software Engineer, David Lippa, and our CTO, Jason Vertrees, break down the background, method, and results.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
Real-time Puppies and Ponies - Evolving Indicator Recommendations in Real-timeTed Dunning
This talk describes how indicator-based recommendations can be evolved in real time. Normally, indicator-based recommendations use a large off-line computation to understand the general structure of items to be recommended and then make recommendations in real-time to users based on a comparison of their recent history versus the large-scale product of the off-line computation.
In this talk, I show how the same components of the off-line computation that guarantee linear scalability in a batch setting also give strict real-time bounds on the cost of a practical real-time implementation of the indicator computation.
Spark for Behavioral Analytics Research: Spark Summit East talk by John W uSpark Summit
This presentation reports our experience on using the machine learning techniques in Apache Spark ecosystem to understand the user behavior in a number of applications. In this context, Spark makes the vast computing power of a large high-performance computing system available to the behavioral economists without requiring the application scientists to learn about parallel computing. To illustrate the effectiveness of this approach, we focus on a compute-intensive task of establishing baseline for studying the impact of policies on consumer behavior. The gold standard for this type of baseline is a randomized control group, however, this control group can only provide a group-level reference, not for individual consumers. In many cases, the self-selection bias along with other factors can make it extremely difficult to generate a unbiased control group. By harnessing the computing power of Spark, we are able to learn the behavior pattern for each individual user and therefore create a much more precise baseline for behavioral analysis. We will use two use cases to illustrate the approach: a residential electricity usage study and a traffic pattern prediction study.
What is a real-time recommendation engine? Our Senior Software Engineer, David Lippa, and our CTO, Jason Vertrees, break down the background, method, and results.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
Real-time Puppies and Ponies - Evolving Indicator Recommendations in Real-timeTed Dunning
This talk describes how indicator-based recommendations can be evolved in real time. Normally, indicator-based recommendations use a large off-line computation to understand the general structure of items to be recommended and then make recommendations in real-time to users based on a comparison of their recent history versus the large-scale product of the off-line computation.
In this talk, I show how the same components of the off-line computation that guarantee linear scalability in a batch setting also give strict real-time bounds on the cost of a practical real-time implementation of the indicator computation.
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
On the value of Sampling and Pruning for SBSEJianfeng Chen
Oral Prelim Exam slides (for publication).
Thesis statement: for the optimization of SE planning and replanning tasks, given appropriate separation operators, then oversampling and pruning is better than mutation based evolutionary approaches.
Building multi-modal recommendation engines using search enginesTed Dunning
This is my strata NY talk about how to build recommendation engines using common items. In particular, I show how multi-modal recommendations can be built using the same framework.
Countdown to Zero - Counter Use Cases in AerospikeRonen Botzer
This is my talk from the third Israeli Aerospike User Group meetup. It covers modeling counters, handling hot keys on counters, and implementing accurate counters with Aerospike's strong consistency mode.
Internet of Things Application: SoundsensePeter SHIN
As a graduate course work, I have practiced Raspberry Pi programming and Amazon Web Service utilization. DynamoDB, IoT, EC2, and SES services were used in this project.
The project was to build a device for sound detection, using Kalman Filter and Moving Average methods for analysis
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
Bayesian Inference : Kalman filter 에서 Optimization 까지 - 김홍배 박사님AI Robotics KR
[AI x Robotics : The First] 행사 - 김홍배 박사님 강연
Bayesian Inference : Kalman filter 에서 Optimization 까지
AI Robotics KR
(https://www.facebook.com/groups/airoboticskr/)
Anomaly Detection - New York Machine LearningTed Dunning
Anomaly detection is the art of finding what you don't know how to ask for. In this talk, I walk through the why and how of building probabilistic models for a variety of problems including continuous signals and web traffic. This talk blends theory and practice in a highly approachable way.
Many statistics are impossible to compute precisely on streaming data. There are some very clever algorithms, however, which allow us to compute very good approximations of these values efficiently in terms of CPU and memory.
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
On the value of Sampling and Pruning for SBSEJianfeng Chen
Oral Prelim Exam slides (for publication).
Thesis statement: for the optimization of SE planning and replanning tasks, given appropriate separation operators, then oversampling and pruning is better than mutation based evolutionary approaches.
Building multi-modal recommendation engines using search enginesTed Dunning
This is my strata NY talk about how to build recommendation engines using common items. In particular, I show how multi-modal recommendations can be built using the same framework.
Countdown to Zero - Counter Use Cases in AerospikeRonen Botzer
This is my talk from the third Israeli Aerospike User Group meetup. It covers modeling counters, handling hot keys on counters, and implementing accurate counters with Aerospike's strong consistency mode.
Internet of Things Application: SoundsensePeter SHIN
As a graduate course work, I have practiced Raspberry Pi programming and Amazon Web Service utilization. DynamoDB, IoT, EC2, and SES services were used in this project.
The project was to build a device for sound detection, using Kalman Filter and Moving Average methods for analysis
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
Bayesian Inference : Kalman filter 에서 Optimization 까지 - 김홍배 박사님AI Robotics KR
[AI x Robotics : The First] 행사 - 김홍배 박사님 강연
Bayesian Inference : Kalman filter 에서 Optimization 까지
AI Robotics KR
(https://www.facebook.com/groups/airoboticskr/)
Anomaly Detection - New York Machine LearningTed Dunning
Anomaly detection is the art of finding what you don't know how to ask for. In this talk, I walk through the why and how of building probabilistic models for a variety of problems including continuous signals and web traffic. This talk blends theory and practice in a highly approachable way.
Many statistics are impossible to compute precisely on streaming data. There are some very clever algorithms, however, which allow us to compute very good approximations of these values efficiently in terms of CPU and memory.
High Resolution Energy Modeling that Scales with Apache Spark 2.0 Spark Summi...Spark Summit
As advanced sensor technologies are becoming widely deployed in the energy industry, the availability of higher-frequency data results in both analytical benefits and computational costs. To an energy forecaster or data scientist, some of these benefits might include enhanced predictive performance from forecasting models as well as improved pattern recognition in energy consumption across building types, economic sectors, and geographies. To a utility or electricity service provider, these benefits might include significantly deeper insights into their diverse customer base. However, these advantages can come with a high computational price tag. With Spark 2.0, User-Defined Functions can be applied across grouped SparkDataFrames in the SparkR API to solve the multivariate optimization and model selection problems typically required for fitting site-level models. This recently added feature of Spark 2.0 on Databricks has allowed DNV GL to efficiently fit predictive models that relate weather, electricity, water, and gas consumption across virtually any number of buildings.
Extend Your Journey: Introducing Signal Strength into Location-based Applicat...Chih-Chuan Cheng
Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without adversely impacting user experience. Then, we propose a dynamic-programming algorithm to solve the fundamental problem and prove its optimality in terms of energy savings. We also provide an optimality condition with respect to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
Deep Reinforcement Learning: Q-LearningKai-Wen Zhao
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
In this work, we propose to apply trust region optimization to deep reinforcement
learning using a recently proposed Kronecker-factored approximation to
the curvature. We extend the framework of natural policy gradient and propose
to optimize both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region; hence we call our method Actor Critic using
Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this
is the first scalable trust region natural gradient method for actor-critic methods.
It is also a method that learns non-trivial tasks in continuous control as well as
discrete control policies directly from raw pixel inputs. We tested our approach
across discrete domains in Atari games as well as continuous domains in the MuJoCo
environment. With the proposed methods, we are able to achieve higher
rewards and a 2- to 3-fold improvement in sample efficiency on average, compared
to previous state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines.
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adap...SmartenIT
Tobias Hoßfeld, Michael Seufert, Christian Sieber, Thomas Zinner
Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adaptive Streaming.
6th International Workshop on Quality of Multimedia Experience (QoMEX), Singapore, September 2014.
Abstract:
HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality to the current network conditions, and thus, avoids stalling (i.e., playback interruptions) to the greatest possible extend. The adaptation of video streams is done by switching between different quality representation levels, which influences the user perceived quality of the video stream. In this work, the influence of several adaptation parameters, namely, switch amplitude (i.e., quality level difference), switching frequency, and recency effects, on Quality of Experience (QoE) is investigated. Therefore, crowdsourcing experiments were conducted in order to collect subjective ratings for different adaptation-related test conditions. The results of these subjective studies indicate the influence of the adaptation parameters, and based on these findings a simplified QoE model for HAS is presented, which only relies on the switch amplitude and the playback time of each layer.
An Automatic and On-demand MNO Selection MechanismSmartenIT
A manual selection of the Mobile Network Operator (MNO) to be used on a mobile device is possible through the respective user interface. Furthermore, mobile devices can be adjusted to select automatically the MNO based on the strongest signal strength, among the list of those MNOs the Subscriber Identity Module (SIM) card is allowed to be registered with. However, so far in modern mobile operating systems, such as Android and iOS, there is no available method in the public developer’s Application Programming Interface (API), which allows for an automatic and on-demand selection of the MNO by third- party applications. Recently, various research approaches assume the existence of an automatic and on-demand MNO selection mechanism to achieve different goals, such as breaking the termination rates monopoly (AbaCUS) or minimizing the non-ionizing radiation of mobile/wearable devices. The interest of such a mechanism has been raised three years ago by the Android developers community. Thus, this work here presents an automatic and on-demand MNO selection mechanism, that has been designed and implemented on the Android platform. For evaluation purposes the energy and end-to-end (e2e) time consumption while switching among MNOs using this mechanism is evaluated and as an applied example the data consumption of AbaCUS signaling messages is measured.
Gamification Framework for Personalized Surveys on Relationships in Online So...SmartenIT
Michael Seufert, Karl Lorey, Matthias Hirth, Tobias Hoßfeld
Gamification Framework for Personalized Surveys on Relationships in Online Social Networks.
1st International Workshop on Crowdsourcing and Gamification in the Cloud (CGCloud), Dresden, Germany, December 2013.
Abstract:
The estimation of psychological properties of relationships (e.g., popularity, influence, or trust) only from objective data in online social networks (OSNs) is a rather vague approach. A subjective assessment produces more accurate results, but it requires very complex and cumbersome surveys. The key contribution of this paper is a framework for personalized surveys on relationships in OSNs which follows a gamification approach. A game was developed and integrated into Facebook as an app, which makes it possible to obtain subjective ratings of users' relationships and objective data about the users, their interactions, and their social network. The combination of both subjective and objective data facilitates a deeper understanding of the psychological properties of relationships in OSNs, and lays the foundations for future research of subjective aspects within OSNs.
Talk on "Socially-aware Traffic Management" given by Michael Seufert (http://www3.informatik.uni-wuerzburg.de/staff/michael.seufert/) at the workshop Sozioinformatik 2013 (http://www.sozioinformatik2013.de/, organized by Katharina Anna Zweig; held in conjunction with Jahrestagung der Gesellschaft für Informatik (INFORMATIK 2013)). The workshop addressed questions evolving around the interplay between social and technical systems, and bridged the gap from social sciences to computer sciences. The workshop talks gave an overview on different aspects of interactions between humans and IT-systems, and highlighted the need for a combination of social sciences and computer science in this field. The workshop showed that it is possible and sometimes necessary to integrate social studies into the design and application of IT-systems. This applies to SmartenIT especially in the context of socially-aware traffic management.
Michael Seufert, George Darzanos, Valentin Burger, Ioanna Papafili, Tobias Hoßfeld
Socially-Aware Traffic Management.
Workshop Sozioinformatik 2013, Koblenz, Germany, September 2013.
Abstract:
Socially-aware traffic management exploits social signals to optimize traffic management in the Internet in terms of traffic load, energy consumption, or end-user satisfaction. Several use cases can benefit from socially-aware traffic management and the performance of overlay applications can be enhanced. In the talk we show interdisciplinary efforts between communication networks and social network analysis. Specifically, we give an overview on existing use cases and solutions, but also raise discussions at the workshop on additional benefits from the integration of social information into traffic management.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath