Trifacta Data Preparation for Amazon Redshift and S3 Must Be Deployed Into an Existing Virtual Private Cloud (VPC)
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Zack Robinson-Android and Amazon Resume.Docx
Contact Innovative, Insightful, Resilient Phone: 814-525-1519 A geek with a gift for gab Email: [email protected] 8 years in Software Development Strengths Summary of Expertise Mobile/TV App Development ▪ Rapidly creates custom features for Android and Amazon Software Engineering Principles applications using UI/UX requirements and mockups Object-Oriented Programming ▪ Particularly comfortable with video playback, location services, (Java) catalog management, authentication, payment processing, and Functional Programming (Kotlin) user management features Refactoring to Design Patterns ▪ Expert at writing clean, re-usable Java and Kotlin code using SOLID principles and software design patterns Data Structures and Algorithms ▪ Adept at reducing operational costs on projects by automating Test-Driven Development quality assurance tasks Technical Communication ▪ Knowledgeable on Android Architecture Components and test Requirements Analysis driven frameworks (MVVM, MVP, etc) Strategic Consulting ▪ Familiar with NFC (Near field communication) technology, Broadcast Receivers and Services, and 3G and Wi-Fi technology. ▪ Adept at storing JSON server responses as data models in device memory (shared preferences, external storage, SQL Lite DB, etc.) ▪ Maintains quality through rigorous code review and testing, and partnerships with QA teams. ▪ Excellent at communicating technical requirements to non-technical stakeholders. ▪ Comfortable working remotely or on-site Technical Skills and Knowledge Languages: Java, Kotlin, Bytecode, XML, SQL, JavaScript, -
Poly Videoos Offer of Source for Open Source Software 3.6.0
OFFER OF SOURCE FOR 3.6.0 | 2021 | 3725-85857-010A OPEN SOURCE SOFTWARE August Poly VideoOS Software Contents Offer of Source for Open Source Software .............................................................................. 1 Open Source Software ............................................................................................................. 2 Qualcomm Platform Licenses ............................................................................................................. 2 List of Open Source Software .................................................................................................. 2 Poly G7500, Poly Studio X50, and Poly Studio X30 .......................................................................... 2 Poly Microphone IP Adapter ............................................................................................................. 13 Poly IP Table Microphone and Poly IP Ceiling Microphone ............................................................. 18 Poly TC8 and Poly Control Application ............................................................................................. 21 Get Help ..................................................................................................................................... 22 Related Poly and Partner Resources ..................................................................................... 22 Privacy Policy ........................................................................................................................... -
A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem
Noname manuscript No. (will be inserted by the editor) A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem César Soto-Valero · Nicolas Harrand · Martin Monperrus · Benoit Baudry Received: date / Accepted: date Abstract Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application’s code and its ex- ternal dependencies, and automate several software development tasks. How- ever, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application’s compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We ana- lyze 9; 639 Java artifacts hosted on Maven Central, which include a total of 723; 444 dependency relationships. Our key result is that 75:1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1=4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total. -
Gateway Licensing Information User Manual Version 19
Gateway Licensing Information User Manual Version 19 December 2019 Contents Introduction ...................................................................................................................................... 5 Licensed Products, Restricted Use Licenses, and Prerequisite Products ........................................ 5 Primavera Gateway ................................................................................................................................ 5 Third Party Notices and/or Licenses ................................................................................................ 6 Bootstrap ................................................................................................................................................ 6 Commons Codec .................................................................................................................................... 6 Commons Compress .............................................................................................................................. 6 Commons IO ........................................................................................................................................... 7 Commons Net ......................................................................................................................................... 7 commons-vfs .......................................................................................................................................... 7 HttpComponents HttpClient .................................................................................................................. -
Advanced Model Deployments with Tensorflow Serving Presentation.Pdf
Most models don’t get deployed. Hi, I’m Hannes. An inefficient model deployment import json from flask import Flask from keras.models import load_model from utils import preprocess model = load_model('model.h5') app = Flask(__name__) @app.route('/classify', methods=['POST']) def classify(): review = request.form["review"] preprocessed_review = preprocess(review) prediction = model.predict_classes([preprocessed_review])[0] return json.dumps({"score": int(prediction)}) Simple Deployments @app.route('/classify', methods=['POST']) Why Flask is insufficient def classify(): review = request.form["review"] ● No consistent APIs ● No consistent payloads preprocessed_review = preprocess(review) ● No model versioning prediction = model.predict_classes( ● No mini-batching support [preprocessed_review])[0] ● Inefficient for large models return json.dumps({"score": int(prediction)}) Image: Martijn Baudoin, Unsplash TensorFlow Serving TensorFlow Serving Production ready Model Serving ● Part of the TensorFlow Extended Ecosystem ● Used internally at Google ● Highly scalable model serving solution ● Works well for large models up to 2GB TensorFlow 2.0 ready! * * With small exceptions Deploy your models in 90s ... Export your Model import tensorflow as tf TensorFlow 2.0 Export tf.saved_model.save( ● Consistent model export model, ● Using Protobuf format export_dir="/tmp/saved_model", ● Export of graphs and signatures=None estimators possible ) $ tree saved_models/ Export your Model saved_models/ └── 1555875926 ● Exported model as Protobuf ├── assets (Saved_model.pb) -
An Evaluation of Tensorflow As a Programming Framework for HPC Applications
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN Master in Computer Science Date: August 28, 2018 Supervisor: Stefano Markidis Examiner: Erwin Laure Swedish title: En undersökning av TensorFlow som ett utvecklingsramverk för högpresterande datorsystem School of Electrical Engineering and Computer Science iii Abstract In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and perfor- mance. At the core of these application is dense matrix-matrix multipli- cation. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabili- ties. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. -
Evil Pickles: Dos Attacks Based on Object-Graph Engineering∗
Evil Pickles: DoS Attacks Based on Object-Graph Engineering∗ Jens Dietrich1, Kamil Jezek2, Shawn Rasheed3, Amjed Tahir4, and Alex Potanin5 1 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 2 NTIS – New Technologies for the Information Society Faculty of Applied Sciences, University of West Bohemia Pilsen, Czech Republic [email protected] 3 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 4 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 5 School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand [email protected] Abstract In recent years, multiple vulnerabilities exploiting the serialisation APIs of various programming languages, including Java, have been discovered. These vulnerabilities can be used to devise in- jection attacks, exploiting the presence of dynamic programming language features like reflection or dynamic proxies. In this paper, we investigate a new type of serialisation-related vulnerabilit- ies for Java that exploit the topology of object graphs constructed from classes of the standard library in a way that deserialisation leads to resource exhaustion, facilitating denial of service attacks. We analyse three such vulnerabilities that can be exploited to exhaust stack memory, heap memory and CPU time. We discuss the language and library design features that enable these vulnerabilities, and investigate whether these vulnerabilities can be ported to C#, Java- Script and Ruby. We present two case studies that demonstrate how the vulnerabilities can be used in attacks on two widely used servers, Jenkins deployed on Tomcat and JBoss. -
Executable Trigger-Action Comments
Executable Trigger-Action Comments Pengyu Nie, Rishabh Rai, Junyi Jessy Li, Sarfraz Khurshid, Raymond J. Mooney, and Milos Gligoric The University of Texas at Austin {pynie@,rrai.squared@,jessy@austin.,khurshid@ece.,mooney@cs.,gligoric@}utexas.edu ABSTRACT Wave project: “Remove this when HtmlViewImpl implements getAt- Natural language elements, e.g., todo comments, are frequently tributes”, etc.). We consider those comments where the trigger is used to communicate among the developers and to describe tasks expressed based on the state of the code repositories and actions that need to be performed (actions) when specific conditions hold require modifications of source or binary code. We call these com- in the code repository (triggers). As projects evolve, development ments trigger-action comments. processes change, and development teams reorganize, these com- Although trigger-action comments are ubiquitous, they are, like ments, because of their informal nature, frequently become irrele- other types of comments, written in natural language. Thus, as vant or forgotten. projects evolve, development processes change, and development We present the first technique, dubbed TrigIt, to specify trigger- teams reorganize, these comments frequently become irrelevant or action todo comments as executable statements. Thus, actions are forgotten. As an example, consider the following comment from executed automatically when triggers evaluate to true. TrigIt spec- the Apache Gobblin project [13]: “Remove once we commit any ifications are written in the host language (e.g., Java) and are evalu- other classes”. This comment, followed by an empty class, was in- ated as part of the build process. The triggers are specified as query cluded in revision 38ce024 (Dec 10, 2015) in package-info.java file statements over abstract syntax trees and abstract representation to force the javadoc tool to generate documentation for an empty of build configuration scripts, and the actions are specified as code package. -
Regeldokument
Master’s degree project Source code quality in connection to self-admitted technical debt Author: Alina Hrynko Supervisor: Morgan Ericsson Semester: VT20 Subject: Computer Science Abstract The importance of software code quality is increasing rapidly. With more code being written every day, its maintenance and support are becoming harder and more expensive. New automatic code review tools are developed to reach quality goals. One of these tools is SonarQube. However, people keep their leading role in the development process. Sometimes they sacrifice quality in order to speed up the development. This is called Technical Debt. In some particular cases, this process can be admitted by the developer. This is called Self-Admitted Technical Debt (SATD). Code quality can also be measured by such static code analysis tools as SonarQube. On this occasion, different issues can be detected. The purpose of this study is to find a connection between code quality issues, found by SonarQube and those marked as SATD. The research questions include: 1) Is there a connection between the size of the project and the SATD percentage? 2) Which types of issues are the most widespread in the code, marked by SATD? 3) Did the introduction of SATD influence the bug fixing time? As a result of research, a certain percentage of SATD was found. It is between 0%–20.83%. No connection between the size of the project and the percentage of SATD was found. There are certain issues that seem to relate to the SATD, such as “Duplicated code”, “Unused method parameters should be removed”, “Cognitive Complexity of methods should not be too high”, etc. -
From XML to Flat Buffers: Markup in the Twenty-Teens Warning! the Contenders
Elliotte Rusty Harold [email protected] August 2018 From XML to Flat Buffers: Markup in the Twenty-teens Warning! The Contenders ● XML ● JSON ● YAML ● EXI ● Protobufs ● Flat Protobufs XML JSON YAML EXI Protobuf Flat Buffers App Engine X X Standard Java App Engine X Flex What Uses What Kubernetes X X From technology, tools, and systems Eclipse X I use frequently. There are many others. Maven X Ant X Google X X X X X “APIs” Publishing X XML XML ● Very well defined standard ● By far the most general format: ○ Mixed content ○ Attributes and elements ● By far the best tool support. Nothing else is close: ○ XSLT ○ XPath ○ Many schema languages: ■ W3C XSD ■ RELAX NG More Reasons to Choose XML ● Most composable for mixing and matching markup; e.g. MathML+SVG in HTML ● Does not require a schema. ● Streaming support: very large documents ● Better for interchange amongst unrelated parties ● The deeper your needs the more likely you’ll end up here. Why Not XML? ● Relatively complex for simple tasks ● Limited to no support for non-string programming types: ○ Numbers, booleans, dates, money, etc. ○ Lists, maps, sets ○ You can encode all these but APIs don’t necessarily recognize or support them. ● Lots of sharp edges to surprise the non-expert: ○ 9/10 are namespace related ○ Attribute value normalization ○ White space ● Some security issues if you’re not careful (Billion laughs) JSON ● Simple for object serialization and program data. If your data is a few basic types (int, string, boolean, float) and data structures (list, map) this works well. ● More or less standard (7-8 of them in fact) ● Consumption libraries for essentially all significant languages Why Not JSON? ● It is surprising how fast needs grow past a few basic types and data structures. -
Portable Stateful Big Data Processing in Apache Beam
Portable stateful big data processing in Apache Beam Kenneth Knowles Apache Beam PMC Software Engineer @ Google https://s.apache.org/ffsf-2017-beam-state [email protected] / @KennKnowles Flink Forward San Francisco 2017 Agenda 1. What is Apache Beam? 2. State 3. Timers 4. Example & Little Demo What is Apache Beam? TL;DR (Flink draws it more like this) 4 DAGs, DAGs, DAGs Apache Beam Apache Flink Apache Cloud Hadoop Apache Apache Dataflow Spark Samza MapReduce Apache Apache Apache (paper) Storm Gearpump Apex (incubating) FlumeJava (paper) Heron MillWheel (paper) Dataflow Model (paper) 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Java fully managed input.apply( Apache Spark Sum.integersPerKey()) local, on-prem, cloud Sum Per Key Apache Apex Python local, on-prem, cloud input | Sum.PerKey() Apache Gearpump (incubating) ⋮ ⋮ 6 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Python fully managed input | KakaIO.read() Apache Spark local, on-prem, cloud KafkaIO Apache Apex ⋮ local, on-prem, cloud Apache Java Gearpump (incubating) class KafkaIO extends UnboundedSource { … } ⋮ 7 The Beam Model PTransform Pipeline PCollection (bounded or unbounded) 8 The Beam Model What are you computing? (read, map, reduce) Where in event time? (event time windowing) When in processing time are results produced? (triggers) How do refinements relate? (accumulation mode) 9 What are you computing? Read ParDo Grouping Composite Parallel connectors to Per element Group -
Cloud Native Communication Patterns with Grpc
Cloud Native Communication Patterns with gRPC Kasun Indrasiri Author “gRPC Up and Running” and “Microservices for Enterprise” About Me ● Author “gRPC Up & Running”, “Microservices for Enterprise” ● Product Manager/Senior Director at WSO2. ● Committer and PMC member at Apache Software Foundation. ● Founder “Bay area Microservices, APIs and Integration” meetup group. What is gRPC? ● Modern Inter-process communication technology. ● Invoking remote functions as easy as making a local function invocation. ● Contract-first. ● Binary messaging on the wire on top of HTTP2 ● Polyglot. Fundamentals of gRPC - Service Definition syntax = "proto3"; ● Defines the business capabilities of package ecommerce; your service. service ProductInfo { rpc addProduct(Product) returns (ProductID); ● Protocol Buffers used as the IDL for rpc getProduct(ProductID) returns (Product); define services. } message Product { ● Protocol Buffers : string id = 1; ○ A language-agnostic, platform-neutral, string name = 2; extensible mechanism to serializing string description = 3; float price = 4; structured data. } ● Defines service, remote methods, and message ProductID { data types. string value = 1; } ProductInfo.proto Fundamentals of gRPC - gRPC Service // AddProduct implements ecommerce.AddProduct ● gRPC service implements the func (s *server) AddProduct(ctx context.Context, in *pb.Product) (*pb.ProductID, business logic. error) { ● Generate server side skeleton from // Business logic } service definition. // GetProduct implements ecommerce.GetProduct func (s *server) GetProduct(ctx