Android developer tools
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This document provides information about Android developer tools including the emulator, dumpsys, procrank, traceview, systrace, tracer, lint, hprof, MAT, and ddmlib. It discusses capabilities of the emulator such as VM acceleration, GPU acceleration, compatibility across API levels, hardware acceleration benchmarks, scaling, and telnet interface. It also provides examples of using dumpsys, procrank, and ddmlib.
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1. It describes the Android software stack and the structure of the Android Open Source Project code.
2. It discusses requirements for building and accessing the AOSP code, including using the 'repo' tool to download the source.
3. It summarizes the AOSP code structure and highlights key components like the Linux kernel, HAL, and build system.
4. It provides an overview of the Android boot process and offers tips for debugging, including increasing log levels and using tools like logcat, adb, and dumpsys.
Kandroid for nhn_deview_20131013_v5_final
The document provides information about high performance Android app development. It begins with a history of Android performance features from early versions through Jellybean and Project Butter. It then compares the three Android programming models (SDK, NDK, RenderScript) in terms of workflow, execution model, and performance. A case study on the performance features of the Google Chrome browser for Android is presented, covering its multi-process architecture, hardware acceleration, networking, and VSync scheduling. The document concludes with a questionnaire on topics like multi-core vs GPU, Android vs Chrome, and developments beyond Android.
Porting Tizen-IVI 3.0 to an ARM based SoC Platform
Porting Tizen IVI 3.0 to run on ARM-based Renesas R-Car platforms involved replacing the Mesa graphics driver with libraries supporting the PowerVR GPU, implementing a Wayland buffer manager for WebKit web processes, and customizing GStreamer to use hardware-accelerated video decoding with contiguous memory buffers allocated through libkms. This allowed standard Tizen applications, web apps, and 1080p video playback to run natively on the ARM SoC.
Open source Android 10 on Orange Pi: Meth or Reality?
This document provides an overview of efforts to port Android 10 to inexpensive single board computers like the Orange Pi using only open source components. It discusses the requirements to use the latest Android version, a cheap board, only open source projects, and to have a working solution out of the box. The document outlines the project structure, including mainline kernel with patches, Mesa for graphics, and upstream HWComposer and audio HAL projects. It provides timeline details for graphics and video driver mainlining. Supported boards currently include Orange Pi PC, Plus2e and Raspberry Pi 4, with plans to expand support. Building instructions are provided.
Android Internals
Android Internals talk given ad Android Montreal on Nov. 3rd 2010 by Karim Yaghmour (author of O'Reilly's "Building Embedded Linux Systems.")
A165 tools for java and javascript
This document summarizes an introduction presentation on IBM's monitoring and diagnostic tools for Java and JavaScript. It introduces several reactive and proactive tools, including the Garbage Collection and Memory Visualizer (GCMV) for analyzing garbage collection and memory usage data offline, the Interactive Diagnostic Data Explorer (IDDE) for analyzing crash dumps, and Health Center for low overhead runtime monitoring. It provides an overview of the capabilities and use cases of each tool and how to collect relevant application and runtime data.
Tuning Android for low RAM
Slides from Android Builder's Summit 2014 in San Jose, CA
The 4.4 KitKat release includes the results of “Project Svelte”: a set of tweaks to the operating system to make it run more easily on devices with around 512 MiB RAM. This is likely to be especially important for people working with “Embedded Android”, that is, implementing Android on devices that are not smart phones or tablets.
Android containerization in brief
This document describes, in brief, the technical approach and system architecture adopted in ICL/ITRI Android Containerization open source project.
The Next Leap in JavaScript Performance
The document discusses advances in JavaScript performance, including exposing the full power of hardware to JavaScript through techniques like SIMD.js. It describes how improvements in areas like parallel parsing and compilation of JavaScript, parallelizing the browser layout engine, and SIMD support in JavaScript can help close the performance gap with native code. The talk also covers bringing perceptual computing and Internet of Things capabilities to the web platform through standards like the Media Capture Depth Stream extension.
Coscup2018 itri android-in-cloud
The document discusses Android containerization using Linux container (LXC) technology. It describes how LXC can be used to run multiple Android instances within containers on a single device. Key points include using Linux namespaces to isolate containers, virtualizing Android devices and binder IPC using namespaces, and challenges around scheduling and resource management with multiple containerized Androids. The document also summarizes some open source projects from ITRI related to containerization and virtualization technologies.
Porting Android
This document discusses porting Android to new hardware. It covers components that need to be ported like the bootloader, Linux kernel, and hardware libraries. It also discusses getting the Android Open Source Project code, developing device drivers, customizing the user-space, and building the Android system. The goal is to provide guidance on porting each part of the Android software stack to new CPU architectures and hardware boards.
Q4.11: Porting Android to new Platforms
This document provides an overview of porting Android to new platforms. It discusses the Android software stack, the Android Open Source Project structure, the AOSP code structure, common Android hardware abstraction layers, device configuration files, the AOSP build process, the Android boot process, and Android debugging tools.
[Ultracode Munich Meetup #7] Building Apps for Nexus Player & Android TV
Slides by Mihai Risca, Software Application Engineer at Intel at the Ultracode Munich Meetup #7 @ Werk1 Munich
http://www.meetup.com/Ultracode-Munich/events/219689131/
An Introduction To Android
This document provides an overview of the Android mobile platform architecture. It describes that Android is based on the Linux kernel with additional enhancements for power management, inter-process communication, and more. It also discusses the Dalvik virtual machine, core libraries, and hardware abstraction layer. Finally, it covers aspects of application development including setting up the manifest, laying out UI with XML, and debugging apps.
Similar to Android developer tools (20)
Open-source Android 10 on Orange Pi: myth or reality?
Open-source Android 10 on Orange Pi: myth or reality?
Porting Tizen-IVI 3.0 to an ARM based SoC Platform
Porting Tizen-IVI 3.0 to an ARM based SoC Platform
Open source Android 10 on Orange Pi: Meth or Reality?
Open source Android 10 on Orange Pi: Meth or Reality?
[Ultracode Munich Meetup #7] Building Apps for Nexus Player & Android TV
[Ultracode Munich Meetup #7] Building Apps for Nexus Player & Android TV
More from inovex GmbH
lldb – Debugger auf Abwegen
lldb kann mehr als nur einfache Breakpoints oder po. In dem Vortrag zeigt Oliver Bayer, wie sich mit Hilfe von lldb Programmcode zur Ausführungszeit manipulieren lässt, ohne das hierfür der Sourcecode anzupassen ist. Sei es, damit Test- oder Debugcode nicht in die produktiv App gelangt, oder weil der Sourcecode für einen Teil der App nicht vorliegt.
Event: macoun, 04.10.2019
Speaker: Oliver Bayer, inovex
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Are you sure about that?! Uncertainty Quantification in AI
With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.
Speaker: Dr. Florian Wilhelm, Simon Bachstein, inovex
Event: PyCon/PyData Berlin 2019
Datum: 10.10.2019
Mehr Tech-Vorträge: inovex.de/vortraege
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Why natural language is next step in the AI evolution
In 2010 ImageNet finally ended the AI winter and gave machines the sense of sight. Within the following years dramatic improvements in tasks such as image classification and object detection lead to innovations like face ID and autonomous driving. Recently, similar developments happened in the field of natural language. Using Attention mechanism and transformers tasks such as question answering and text summarization reached new benchmarks.
This talk will not only explain those, but point out how Transfer Learning and open source models such as Google Bert will open the field to new innovations in AI.
Speaker: Nico Kreiling, inovex
Event: AIxIA, 01.10.2019
Mehr Tech-Vorträge: inovex.de/vortraege
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WWDC 2019 Recap
Die Worldwide Developers Conference (WWDC) ist eine von Apple jährlich durchgeführte Konferenz für Software-Entwickler (MacOS, iOS und WatchOS). Um die WWDC 2019 nochmal Revue passieren zu lassen, wurde beim Mobile Development Karlsruhe Meetup zu einer offenen Diskussionsrunde eingeladen. Die Slides fassen die für inovexler Philipp interessantesten Neuigkeiten der WWDC2019 zusammen und dienten beim Meetup als Diskussionsgrundlage.
Event: 9. Mobile Development Meetup (WWDC Edition)
Speaker: Philipp Wallrich, inovex
Datum: 17.06.2019
Mehr Tech-Vorträge: inovex.de/vortraege
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Network Policies
Trust is good, control is better – A short story about Network Policies.
Abstract:
Probably everybody who uses Kubernetes in a productive environment with multiple users possibly has looked at policies. Often the operators of the cluster(s) just trust the policies but in some cases it might be useful to control if the policies actually have taken action and often there are just to many Policies in the cluster setup to manually test them all (and obviously you don’t want to do this). Testing the effectiveness of the Network Policies can be done in different approaches. In this talk we will show you the benefits and drawbacks of different approaches and what solution we finally chose. Also we will show you some other tools and how they complement our solution. As a takeaway you will get an overview of different testing strategies for policies, as well as understanding challenges in testing policies in general and the Kubernetes ecosystem.
Event: ContainerDays 2019
Datum: 26.06.2019
Speaker: Johannes M. Scheuermann, Maximilian Bischoff (beide inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
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Interpretable Machine Learning
Interpretierbarkeit von ML-Modellen hat die Zielsetzung, die Ursachen einer Prognose offenzulegen und eine daraus abgeleitete Entscheidung für einen Menschen nachvollziehbar zu erklären. Durch die Nachvollziehbarkeit von Prognosen lässt sich beispielsweise sicherstellen, dass deren Herleitung konsistent zum Domänenwissen eines Experten ist. Auch ein unfairer Bias lässt sich durch die Erklärung aussagekräftiger Beispiele identifizieren.
Prognosemodelle lassen sich grob in intrinsisch interpretierbare Modelle und nicht-interpretierbare (auch Blackbox-) Modelle unterscheiden. Intrinsisch interpretierbare Modelle sind dafür bekannt, dass sie für einen Menschen leicht nachvollziehbar sind. Ein typisches Beispiel für ein solches Modell ist der Entscheidungsbaum, dessen regelbasierter Entscheidungsprozess intuitiv und leicht zugänglich ist. Im Gegensatz dazu gelten Neuronale Netze als Blackbox-Modelle, deren Prognosen durch die komplexe Netzstruktur schwer nachvollziehbar sind.
In diesem Talk erläuterte Marcel Spitzer das Konzept von Interpretierbarkeit im Kontext von Machine Learning und stellte gängige Verfahren zur Interpretation von Modellen vor. Besonderen Fokus legte er dabei auf modellunabhängige Verfahren, die sich auch auf prognosestarke Blackbox-Modelle anwenden lassen.
Event: M3 Minds Mastering Machines
Speaker: Marcel Spitzer
Blog-Artikel: https://www.inovex.de/blog/machine-learning-interpretability/
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Jenkins X – CI/CD in wolkigen Umgebungen
Das Ökosystem rund um Kubernetes wächst täglich. Insbesondere cloud-native Continuous-Deployment-Strategien stehen Hoch im Kurs und werden in diversen Open-Source-Projekten vorangetrieben. In einer Reihe von Evalutionen nimmt inovex diese Tools genauer unter die Lupe - den Anfang macht Jenkins X.
Jenkins X wurde im März 2018 veröffentlicht. Das Konzept hinter dem Tool ist primär, bestehende Teillösungen (Helm, Skaffold, Prow, Tekton) einzusetzen, um sie abstrahiert in ein Kommandozeilen-Interface zu packen. Der Vortrag beschreibt sowohl die klassische Architektur als auch den "Severless"-Ansatz. Des weiteren werden das Kommandozeilen-Tool "jx", der allgemeine Entwicklungs-Workflow sowie diverse Features vorgestellt.
Bei unseren Tests im Rahmen der Evaluation sind uns einige Stolpersteine aufgefallen. Es sind vor allem die vielen eingesetzten Dritt-Tools, die den Betrieb und den Upkeep eines mit Jenkins X erstellten Clusters verkomplizieren. Als Fazit stellen wir Jenkins X im Mai 2019 ein "befriedigend" aus und beobachten gespannt, wie sich das Tool in den kommmenden Monaten und Jahren weiterentwickeln wird.
Event: Talk4Nerds, 29.04.2019
Speaker: Simon Kienzler, Johannes M. Scheuermann (beide inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
AI auf Edge-Geraeten
Neben dem großen Machine-Learning-Trend in der Cloud zeichnet sich zunehmend die Tendenz ab, bestimmte Aufgaben direkt auf Edge-Geräten auszuführen. Wir erkunden die Vorteile von Auswertungen direkt an der Quelle der Daten und die damit verbundenen Herausforderungen. Denn die Rechenleistung der Cloud steht uns hier leider nicht zur Verfügung.
Zur Lösung stehen uns verschiedene Hardwareoptionen wie CPUs, GPUs, FPGAs oder spezielle ASICs und Frameworks zur Verfügung, die wir am Beispiel von einem Convolutional Neural Network evaluieren. Dabei gibt es praktische Tipps und Erfahrungen aus realen Projekten sowie anschauliche Demos auf verschiedenen Hardwareplattformen.
Vorkenntnisse:
Vorkenntnisse über tiefe neuronale Netze sind von Vorteil.
Lernziele:
- Verständnis über die Vorteile von AI auf Edge-Geräten und den damit verbundenen Herausforderungen.
- Wissen über die verschiedenen Hard- und Softwarelösungen erlangen, um diese in eigenen Projekten einzusetzen.
Event: building IoT, 03.04.2019
Speaker: Dominik Helleberg, inovex
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Blog-Artikel: inovex.de/blog
Prometheus on Kubernetes
This document discusses Prometheus on Kubernetes. It provides an overview of Prometheus and its ecosystem, including how it is used for service discovery on Kubernetes, collecting and storing metrics, ensuring high availability and scalability through sharding, and defining and alerting on service level agreements. It also covers instrumentation using exporters, the Prometheus query language PromQL, and components of the Prometheus ecosystem like Grafana and Alertmanager.
Deep Learning for Recommender Systems
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
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Azure IoT Edge
In seinem Meetup Talk berichtete Maximilian von den aktuellen Problemen von Cloud Computing – insbesondere im Internet of Things – und wie diese durch Edge Computing mitigiert werden können. Er erklärte, wie eine generische Edge-Computing-Architektur aussehen kann und zeigte Anwendungsfälle, von denen manche auch schon in existierenden Produkten umgesetzt sind.
Im Anschluss stellte er Azure IoT Edge vor und erläuterte, wie es das bestehende IoT Framework von Microsoft erweitert sowie die Grundkonzepte, die IoT Edge bereitstellt. Auch die Probleme in dem noch jungen Produkt wurden angesprochen, aber auch die Vorteile und Features, die es liefert.
In der gemeinsamen Demo mit Eli haben dann beide Speaker die technischen Details von Azure IoT Edge gezeigt und demonstriert, beispielsweise wie Code automatisiert von einer CI/CD-Pipeline in Azure DevOps auf ein IoT-Gerät deployed werden kann.
Event: inovex Meetup, 12.03.& 19.03.2019
Speaker: Maximilian Bischoff, inovex
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Representation Learning von Zeitreihen
Es liegt in der Natur des Menschen das Unvorhersehbare vorherzusagen: Wetter, Aktienkurse, Krankheitsverläufe, die Reaktion eines Menschen. Neueste Deep Learning Ansätze sind in der Lage solche sequentielle Sachverhalte immer genauer zu prognostizieren, setzen aber auch immer größere Datenmengen und Rechenleistungen voraus, die sowohl in Forschung als auch in der Praxis häufig nicht vorliegen. Wie kann man gute Ergebnisse erreichen, wenn nur wenig Daten vorliegen?
Marisa Mohr stellte in ihrem Vortrag einen neuen und vielversprechenden informationstheoretischen Ansatz zum Feature Learning von sequentiellen Daten vor, der potenziell auch mit wenigen Daten auskommt. Dabei ging es speziell um ordinale Muster in Zeitreihen, wie sie beispielsweise als Veränderung von Emotionen im Gesprächsverlauf zu finden sind. Eine solche Entwicklung ist für Menschen in der Regel leicht zu erkennen. Chatbots hingegen können nicht intuitiv auf solche Emotionsverläufe reagieren, sondern müssen entsprechend programmiert werden.
Details:
Deep-Learning-Ansätze wie LSTMs, RNNs oder TCNs haben sich im Umgang mit sequentiellen Daten bewährt. Neuronale Netzwerke sind tief im technischen Sinn, weil sie mehrere (verborgene) Schichten besitzen, aber nicht weil sie ein tiefes Verständnis von Problemen entwickeln. In diesem Vortrag stellte Marisa einen symbolischen informationstheoretischen Ansatz des Representation Learnings von Zeitreihen vor und damit eine Möglichkeit, konzeptionelle Schichten zu konstruieren. Die Idee hinter der sogenannten Permutationsentropie besteht darin, anstelle der Werte einer Zeitreihe die Ordnungsrelation zwischen den Werten zu betrachten, und so auf das natürliche Auf und Ab des zugrundeliegenden dynamischen Systems zurückzugreifen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speakerin: Marisa Mohr (inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Talk to me – Chatbots und digitale Assistenten
Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen.
In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speaker: Anna Weißhaar (inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Künstlich intelligent?
Nicht zuletzt durch die medienwirksame Erfolge des maschinellen Lernens durch DeepMind, OpenAI und Kollegen ist Künstliche Intelligenz im Moment wieder in aller Munde. Einerseits locken zahlreiche neue, vorher undenkbare Anwendungen wie die automatische Diagnose von Krankheiten, autonome Fahrzeuge und Drohnen, oder die automatische Übersetzung gesprochener Wörter. Andererseits warnen mahnenden Stimmen wird vor dem zunehmendem Einflussnahme der „Algorithmen“ auf fast alle Bereiche unseres Lebens sowie vor unerwünschten Folgen von sich verselbstständigenden Computern gewarnt. Einige träumen von – oder fürchten sich vor – der vermeintlich unausweichlichen Singularität, an der sich nichts weniger als das Schicksal der gesamten Menschheit entscheiden wird. Doch was verbirgt sich hinter dem Begriff Künstliche Intelligenz? Je nachdem, wen man fragt, erhält man unterschiedliche, bisweilen gegensätzliche Antworten. Dieser Vortrag stellt einige dieser Antworten vor und versucht sie (nicht nur) anhand von Beispielen aus Forschung und Anwendung einzuordnen.
Event: Business Analytics Day, 07.03.2019
Speaker: Dr. Matthias Richter, Dr. Stefan Igel (inovex)
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Dev + Ops = Go
In den letzten drei Jahren haben wir die Infrastruktur der Fernseh-Plattform waipu.tv gebaut. Dabei haben wir angefangen Tools für den Betrieb in Golang zu schreiben. Aus einigen der Tools wurden Core-Services, die auch die Last einer Fußball-WM-Übertragung locker wegstecken. Wir wollen euch zeigen, wie wir mit der selben Tool-Chain (Golang & Co) Betriebs-Probleme lösen und kritische Business-Applikationen entwickeln. Klassisch DevOps oder Golden Hammer?
Speaker: Christoph Petrausch, Igor Lankin (beide inovex)
Event: DevOpsConference, 04.12.2018
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Das Android Open Source Project
Das Android Open Source Project, kurz AOSP, ist das Betriebssystem, das auf den meisten heutigen und wahrscheinlich auch auf deinem Smartphone läuft. Es ist die Basis für das Android-App-Universum und wird von Millionen Nutzern und Entwicklern auf der Welt verwendet. Wegen der offenen Verfügbarkeit des Source Codes ist es auch die Basis für bekannte Custom ROMs wie LineageOS.
Der erste Teil des Talks gab eine Übersicht über die Architektur des Betriebssystems, das App-Ökosystem, den Hardware Abstraction Layer (HAL), die Sicherheitskonzepte und einige neue Betriebssystementwicklungen wie Project Treble in Android 8.0.
Der zweite Teil des Talks gab einen Einblick in den Quellcode und die Struktur des AOSP: Wie lädt man sich den Source Code herunter, wie baut man das AOSP für unterstützte Geräte und wie kann man die eigenen ROMs auf ein Smartphone flashen? Zum Spaß wurde auch noch in einige Implementierungsdetails von Android-App-API-Funktionen geblickt, die man als App Developer schon aufgerufen hat.
Speaker: Stefan Lengfeld, inovex
Event: inovex Meetup Köln, 23.10.2018
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
Machine Learning Interpretability
This document discusses machine learning interpretability. It defines interpretation as giving explanations to humans for machine learning models and decisions. It notes that humans create, are affected by, and demand explanations for decision systems. The document outlines different techniques for model interpretability including intrinsically interpretable models, post-hoc interpretability techniques that provide explanations for black box models, and model-specific and model-agnostic techniques. It provides examples like partial dependence plots, individual conditional expectation, and local surrogate models. It recommends choosing techniques based on the recipient and purpose of explanations.
Performance evaluation of GANs in a semisupervised OCR use case
Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically.
Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data.
What you'll learn:
Understand how semisupervised learning with GANs works
Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data
Gain insight into an interesting OCR use case of an online vehicle marketplace
Event: O'Reilly Artificial Intelligence Conference, London, 11.10.2018
Speaker: Dr. Florian Wilhelm
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
People & Products – Lessons learned from the daily IT madness
IT im 21. Jahrhundert – What a time to be alive! Es gibt einen (unüberschaubaren) Zoo an Methoden und Produkten die uns so viel Freude an der Arbeit bereiten! Sie sind modern, weil sie neu sind. Sie fordern unser Können heraus, weil sie komplex sind. Sie lösen einige Probleme, die wir vorher nicht hatten. Jeder will sie verwenden, weil Google, Netflix & Co. sie propagieren und Hand auf’s Herz: Will nicht jeder gerne so arbeiten wie Google, Netflix & Co.? Aber macht das wirklich Sinn?
In diesem Vortrag blicken wir auf diverse Erkenntnisse aus dem Einsatz agiler Produktentwicklung, DevOps, Continuous Integration/Delivery, Infrastructure as Code, Immutable Infrastructure (bspw. Docker/Kubernetes), Application Logging und Service Monitoring.
Learning Goals:
- Wir müssen den Einsatz von Methoden und Tools an die Menschen ausrichten, die sie (weiter-)entwickeln und benutzen sollen.
- Manchmal lösen wir mit neuen Tools Probleme, die wir vorher nicht hatten.
- Die Suche nach einfachen Lösungen für komplexe Probleme ist essentiell, aber nicht immer einfach.
Event: Continuous Lifecycle, 15.11.2018
Speaker: Arnold Bechtoldt
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
Infrastructure as (real) Code – Manage your K8s resources with Pulumi
Pulumi (pulumi.io) offers an open source platform to create/manage and deploy your infrastructure in realy programming languages like JavaScript/TypeScript, Go and Python. As Cloud platforms the major 3 cloud providers are supported and additionally you can also use Pulumi with OpenStack and Kubernetes to deploy your applications in the cloud.
In this talk we will take a look how Pulumi is different to traditional solutions like Terraform or the Cloud Provider specific solutions (e.g. CloudFormation). The main focus will be on deploying your services on top of Kubernetes. The talk will contain a little theory part about Pulumi, the rest of the talk is more focused on demos and practical parts. One focus of the talk is the difference of Pulumi to kubectl and helm (or to be precise how they complement each other.
As a takeaway of this talk you should understand the basics of Pulumi and know what are the differences to the traditional deployment tools.
Event: CNCF Meetup Hamburg & Stuttgart, 29.10.2018 & 07.11.2018
Speaker: Johannes M. Scheuermann, inovex
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog/
More from inovex GmbH (20)
Are you sure about that?! Uncertainty Quantification in AI
Are you sure about that?! Uncertainty Quantification in AI
Why natural language is next step in the AI evolution
Why natural language is next step in the AI evolution
Performance evaluation of GANs in a semisupervised OCR use case
Performance evaluation of GANs in a semisupervised OCR use case
People & Products – Lessons learned from the daily IT madness
People & Products – Lessons learned from the daily IT madness
Infrastructure as (real) Code – Manage your K8s resources with Pulumi
Infrastructure as (real) Code – Manage your K8s resources with Pulumi
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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
Epistemic Interaction - tuning interfaces to provide information for AI support
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.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Elizabeth Buie - Older adults: Are we really designing for our future selves?
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
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Epistemic Interaction - tuning interfaces to provide information for AI support
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
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20240605 QFM017 Machine Intelligence Reading List May 2024
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GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Android developer tools
- 1. Dominik Helleberg | inovex GmbH Android Developer Tools
- 4. Emulator
- 5. Emulator VM-Acceleration Android 2.3 API 10 x86 Android 4.0.3 API 15 x86 intel HAXM Mac OSX (intel) Windows (intel)
- 6. Emulator VM-Acceleration Android 2.3 API 10 x86 Android 4.0.3 API 15 x86 KVM Linux (intel + AMD)
- 7. Emulator GPU-Acceleration OpenGL Windows / Linux / Mac OSX Android 4 API >=15 x86 / ARM
- 8. Emulator Kompatibilität API Level VM GPU 2.2 (API 8) 2.3 (API 10) 3.X (API 11-13) 4.0.3 (API 15) 4.1.1 (API 16)
- 9. Emulator HW-Acceleration 770 10179 5360 0 2000 4000 6000 8000 10000 12000 Emulator Emulator VM + GPU Galaxy Nexus AnTuTu Benchmark
- 15. Hierarchy Viewer
- 17. shell tools
- 18. dumpsys SurfaceFlinger bluetooth_a2dp entropy meminfo samplingprofiler usb accessibility clipboard gfxinfo mount search vibrator account connectivity hardware netpolicy sensorservice wallpaper activity content input_method netstats simphonebook wifi alarm country_detector iphonesubinfo network_management sip wifip2p appwidget cpuinfo isms nfc statusbar window audio device_policy location notification telephony.registry backup devicestoragemonitor media.audio_flinger package textservices battery diskstats media.audio_policy permission throttle batteryinfo drm.drmManager media.camera phone uimode bluetooth dropbox media.player power usagestats
- 19. dumpsys meminfo – 4.0.3 Applications Memory Usage (kB): Uptime: 3419841 Realtime: 3419841 ** MEMINFO in pid 1866 [de.inovex.samples] ** Shared Private Heap Heap Heap Pss Dirty Dirty Size Alloc Free ------ ------ ------ ------ ------ ------ Native 1168 1024 1108 8492 7430 189 Dalvik 6759 11204 6220 14599 14429 170 Cursor 0 0 0 Ashmem 3002 4 3000 Other dev 4 0 0 .so mmap 582 1724 112 .jar mmap 0 0 0 .apk mmap 152 0 0 .ttf mmap 3 0 0 .dex mmap 200 0 0 Other mmap 498 12 24 Unknown 669 668 624 TOTAL 13037 14636 11088 23091 21859 359 Objects
- 20. dumpsys meminfo – 2.3.3 Applications Memory Usage (kB): Uptime: 1195390 Realtime: 1195390 ** MEMINFO in pid 1323 [de.inovex.samples] ** native dalvik other total size: 8456 5379 N/A 13835 allocated: 8425 2828 N/A 11253 free: 6 2551 N/A 2557 (Pss): 727 322 8415 9464 (shared dirty): 1496 1872 6972 10340 (priv dirty): 652 36 6720 7408 Objects Views: 0 ViewRoots: 0 AppContexts: 0 Activities: 0 Assets: 2 AssetManagers: 2 Local Binders: 6 Proxy Binders: 12 Death Recipients: 1 OpenSSL Sockets: 0 SQL heap: 0 MEMORY_USED: 0
- 21. procrank PID Vss Rss Pss Uss cmdline 7924 50172K 50172K 28401K 24164K de.inovex.samples 1015 48056K 48056K 25635K 21348K com.android.launcher 887 47492K 47492K 22768K 17040K system_server 961 38732K 38732K 16637K 12372K com.android.systemui 794 40040K 40040K 14686K 8736K zygote 993 29860K 29860K 9293K 5452K com.android.phone 974 28776K 28776K 8404K 4716K com.android.inputmethod.latin 793 6832K 6832K 3660K 2760K /system/bin/ surfaceflinger 796 7292K 7292K 3412K 2196K /system/bin/mediaserver 795 2892K 2892K 939K 524K /system/bin/drmserver 790 1276K 1276K 559K 420K /system/bin/netd 797 800K 800K 516K 460K /system/bin/dbus-daemon 804 456K 456K 440K 440K /sbin/adbd 788 904K 904K 412K 348K /system/bin/vold 9050 600K 600K 355K 344K procrank 792 916K 916K 340K 280K /system/bin/rild 799 616K 616K 230K 188K /system/bin/keystore 798 456K 456K 174K 160K /system/bin/installd
- 23. traceview / systrace / tracer
- 24. traceview
- 25. systrace
- 26. systrace
- 27. tracer
- 28. lint
- 29. Hprof / MAT
- 32. DANKE!