The Paper was done in a group of three for the class project of CS 273: Introduction to Machine Learning at UC Irvine. The group members were Prolok Sundaresan, Varad Meru, and Prateek Jain.
Regression is an approach for modeling the relationship between data X and the dependent variable y. In this report, we present our experiments with multiple approaches, ranging from Ensemble of Learning to Deep Learning Networks on the weather modeling data to predict the rainfall. The competition was held on the online data science competition portal ‘Kaggle’. The results for weighted ensemble of learners gave us a top-10 ranking, with the testing root-mean-squared error being 0.5878.
A Scalable Implementation of Deep Learning on Spark (Alexander Ulanov)Alexander Ulanov
Artificial neural networks (ANN) are one of the popular models of machine learning, in particular for deep learning. The models that are used in practice for image classification and speech recognition contain huge number of weights and are trained with big datasets. Training such models is challenging in terms of computation and data processing. We propose a scalable implementation of deep neural networks for Spark. We address the computational challenge by batch operations, using BLAS for vector and matrix computations and reusing the memory for reducing garbage collector activity. Spark provides data parallelism that enables scaling of training. As a result, our implementation is on par with widely used C++ implementations like Caffe on a single machine and scales nicely on a cluster. The developed API makes it easy to configure your own network and to run experiments with different hyper parameters. Our implementation is easily extensible and we invite other developers to contribute new types of neural network functions and layers. Also, optimizations that we applied and our experience with GPU CUDA BLAS might be useful for other machine learning algorithms being developed for Spark.
The slides were presented at Spark SF Friends meetup on December 2, 2015 organized by Alex Khrabrov @Nitro. The content is based on my talk on Spark Summit Europe. However, there are few major updates: update and more details on the parallelism heuristic, experiments with larger cluster, as well as the new slide design.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
Scalable Distributed Real-Time Clustering for Big Data StreamsAntonio Severien
Analyzing and applying machine learning algorithms to a possibly infinite flow of data is a challenging task. This presentation presents the SAMOA framework, which allows the development of machine learning algorithms on top of any distributed stream processing engine. It also demonstrates the development and use of a distributed clustering algorithm based on CluStream using the Apache S4 platform.
Spark as the Gateway Drug to Typed Functional Programming: Spark Summit East ...Spark Summit
Something really exciting and largely unnoticed is going on in the Spark ecosystem. As data scientists and engineers learn Spark, they’re actually all implicitly learning a much older, more general topic: typed functional programming. While Spark itself was built on an accumulation of powerful computer science concepts from functional programming and other areas, developers are often encountering these ideas in the context of Spark for the first time. It turns out that Spark makes an excellent platform for learning concepts like immutability, higher order and anonymous functions, laziness, and monadic operators.
This talk will discuss how Spark can be used as teaching tool, to build skills in areas like typed functional programming. We’ll explore a skill-building curriculum that can be used with a data scientist or engineer who only has experience in imperative, dynamically-typed languages like Python. This curriculum introduces the core concepts of functional programming and type theory, while providing learners the opportunity to immediately apply their skills at massive scale, using the power of Spark’s painless scalability and resilience.
Based on the experience of building machine learning teams at x.ai and other data-centric startups, this curriculum is the foundation of building poly-skilled, highly autonomous team members who can build scalable intelligent systems. We’ll work from foundational concepts of Scala and functional programming towards a fully implemented machine learning pipeline, all using Spark and MLlib. Unique new features of Spark like Datasets and Structured Streaming will be particularly useful in this effort. Using this approach, teams can help members in all roles learn how to use sophisticated programming techniques that ensure correctness at scale. With these skills in their toolbox, data scientists and engineers often find that building powerful machine learning systems is intuitive, easy, and even fun.
A Scalable Implementation of Deep Learning on Spark (Alexander Ulanov)Alexander Ulanov
Artificial neural networks (ANN) are one of the popular models of machine learning, in particular for deep learning. The models that are used in practice for image classification and speech recognition contain huge number of weights and are trained with big datasets. Training such models is challenging in terms of computation and data processing. We propose a scalable implementation of deep neural networks for Spark. We address the computational challenge by batch operations, using BLAS for vector and matrix computations and reusing the memory for reducing garbage collector activity. Spark provides data parallelism that enables scaling of training. As a result, our implementation is on par with widely used C++ implementations like Caffe on a single machine and scales nicely on a cluster. The developed API makes it easy to configure your own network and to run experiments with different hyper parameters. Our implementation is easily extensible and we invite other developers to contribute new types of neural network functions and layers. Also, optimizations that we applied and our experience with GPU CUDA BLAS might be useful for other machine learning algorithms being developed for Spark.
The slides were presented at Spark SF Friends meetup on December 2, 2015 organized by Alex Khrabrov @Nitro. The content is based on my talk on Spark Summit Europe. However, there are few major updates: update and more details on the parallelism heuristic, experiments with larger cluster, as well as the new slide design.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
Scalable Distributed Real-Time Clustering for Big Data StreamsAntonio Severien
Analyzing and applying machine learning algorithms to a possibly infinite flow of data is a challenging task. This presentation presents the SAMOA framework, which allows the development of machine learning algorithms on top of any distributed stream processing engine. It also demonstrates the development and use of a distributed clustering algorithm based on CluStream using the Apache S4 platform.
Spark as the Gateway Drug to Typed Functional Programming: Spark Summit East ...Spark Summit
Something really exciting and largely unnoticed is going on in the Spark ecosystem. As data scientists and engineers learn Spark, they’re actually all implicitly learning a much older, more general topic: typed functional programming. While Spark itself was built on an accumulation of powerful computer science concepts from functional programming and other areas, developers are often encountering these ideas in the context of Spark for the first time. It turns out that Spark makes an excellent platform for learning concepts like immutability, higher order and anonymous functions, laziness, and monadic operators.
This talk will discuss how Spark can be used as teaching tool, to build skills in areas like typed functional programming. We’ll explore a skill-building curriculum that can be used with a data scientist or engineer who only has experience in imperative, dynamically-typed languages like Python. This curriculum introduces the core concepts of functional programming and type theory, while providing learners the opportunity to immediately apply their skills at massive scale, using the power of Spark’s painless scalability and resilience.
Based on the experience of building machine learning teams at x.ai and other data-centric startups, this curriculum is the foundation of building poly-skilled, highly autonomous team members who can build scalable intelligent systems. We’ll work from foundational concepts of Scala and functional programming towards a fully implemented machine learning pipeline, all using Spark and MLlib. Unique new features of Spark like Datasets and Structured Streaming will be particularly useful in this effort. Using this approach, teams can help members in all roles learn how to use sophisticated programming techniques that ensure correctness at scale. With these skills in their toolbox, data scientists and engineers often find that building powerful machine learning systems is intuitive, easy, and even fun.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the second half of the tutorial.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
Think Like Spark: Some Spark Concepts and a Use CaseRachel Warren
A deeper explanation of Spark's evaluation principals including lazy evaluation, the Spark execution environment, anatomy of a Spark Job (Tasks, Stages, Query execution plan) and presents one use case to demonstrate these concepts.
Spark and Shark: Lightning-Fast Analytics over Hadoop and Hive DataJetlore
Spark is an open source cluster computing framework that can outperform Hadoop by 30x through a combination of in-memory computation and a richer execution engine. Shark is a port of Apache Hive onto Spark, which provides a similar speedup for SQL queries, allowing interactive exploration of data in existing Hive warehouses. This talk will cover how both Spark and Shark are being used at various companies to accelerate big data analytics, the architecture of the systems, and where they are heading. We will also discuss the next major feature we are developing, Spark Streaming, which adds support for low-latency stream processing to Spark, giving users a unified interface for batch and real-time analytics.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the second half of the tutorial.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
Think Like Spark: Some Spark Concepts and a Use CaseRachel Warren
A deeper explanation of Spark's evaluation principals including lazy evaluation, the Spark execution environment, anatomy of a Spark Job (Tasks, Stages, Query execution plan) and presents one use case to demonstrate these concepts.
Spark and Shark: Lightning-Fast Analytics over Hadoop and Hive DataJetlore
Spark is an open source cluster computing framework that can outperform Hadoop by 30x through a combination of in-memory computation and a richer execution engine. Shark is a port of Apache Hive onto Spark, which provides a similar speedup for SQL queries, allowing interactive exploration of data in existing Hive warehouses. This talk will cover how both Spark and Shark are being used at various companies to accelerate big data analytics, the architecture of the systems, and where they are heading. We will also discuss the next major feature we are developing, Spark Streaming, which adds support for low-latency stream processing to Spark, giving users a unified interface for batch and real-time analytics.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rurtherford
backscattering spectra and on a benchmark problem of image recognition. It may also be used for efficient feature extraction.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
Hybrid PSO-SA algorithm for training a Neural Network for ClassificationIJCSEA Journal
In this work, we propose a Hybrid particle swarm optimization-Simulated annealing algorithm and present a comparison with i) Simulated annealing algorithm and ii) Back propagation algorithm for training neural networks. These neural networks were then tested on a classification task. In particle swarm optimization behaviour of a particle is influenced by the experiential knowledge of the particle as well as socially exchanged information. Particle swarm optimization follows a parallel search strategy. In simulated annealing uphill moves are made in the search space in a stochastic fashion in addition to the downhill moves. Simulated annealing therefore has better scope of escaping local minima and reach a global minimum in the search space. Thus simulated annealing gives a selective randomness to the search. Back propagation algorithm uses gradient descent approach search for minimizing the error. Our goal of global minima in the task being done here is to come to lowest energy state, where energy state is being modelled as the sum of the squares of the error between the target and observed output values for all the training samples. We compared the performance of the neural networks of identical architectures trained by the i) Hybrid particle swarm optimization-simulated annealing, ii) Simulated annealing and iii) Back propagation algorithms respectively on a classification task and noted the results obtained. Neural network trained by Hybrid particle swarm optimization-simulated annealing has given better results compared to the neural networks trained by the Simulated annealing and Back propagation algorithms in the tests conducted by us.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural N...Scientific Review SR
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been
successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is
extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The
kernel function is a generalization of the distance metric that measures the distance between two data points as the
data points are mapped into a high dimensional space. During the comparing of the four constructed classification
models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as
proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding
performance in this regard
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Ne...Scientific Review
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
Generating Musical Notes and Transcription using Deep LearningVarad Meru
Music has always been the most followed art form, and lot of research had gone into understanding it. In recent years, deep learning approaches for building unsupervised hierarchical representations from unlabeled data have gained significant interest. Progress in fields, such as image processing and natural language processing, has been substantial, but to my knowledge, methods on auditory data for learning representations have not been studied extensively. In this project I try to use two methods for generating music from range of musical inputs such as MIDI to complex WAV formats. I use RNN-RBMs and CDBN to explore music.
Kakuro: Solving the Constraint Satisfaction ProblemVarad Meru
This work was done as a part of the project for the course CS 271: Introduction to Artificial Intelligence (http://www.ics.uci.edu/~kkask/Fall-2014%20CS271/index.html), taught in Fall 2014.
CS295 Week5: Megastore - Providing Scalable, Highly Available Storage for Int...Varad Meru
Slides created as a part of CS 295's week 5 on Transactions and Systems.
CS 295 (Cloud Computing and BigData) at UCI - https://sites.google.com/site/cs295cloudcomputing/
Cassandra - A Decentralized Structured Storage SystemVarad Meru
Slides created as a part of CS 295's week 4 on NoSQL Basics.
CS 295 (Cloud Computing and BigData) at UCI - https://sites.google.com/site/cs295cloudcomputing/
Slides created as a part of CS 295's week 1 on Cloud Computing Basics.
CS 295 (Cloud Computing and BigData) at UCI - https://sites.google.com/site/cs295cloudcomputing/
Live Wide-Area Migration of Virtual Machines including Local Persistent State.Varad Meru
Slides created as a part of CS 295's week 2 on Virtualization in cloud.
CS 295 (Cloud Computing and BigData) at UCI - https://sites.google.com/site/cs295cloudcomputing/
Machine Learning and Apache Mahout : An IntroductionVarad Meru
An Introductory presentation on Machine Learning and Apache Mahout. I presented it at the BigData Meetup - Pune Chapter's first meetup (http://www.meetup.com/Big-Data-Meetup-Pune-Chapter/).
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
Introduction to Mahout and Machine LearningVarad Meru
This presentation gives an introduction to Apache Mahout and Machine Learning. It presents some of the important Machine Learning algorithms implemented in Mahout. Machine Learning is a vast subject; this presentation is only a introductory guide to Mahout and does not go into lower-level implementation details.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
I gave a series of Seminars at the following colleges in Solapur.
1. Walchand Institute of Technology, Solapur.
2. Brahmdevdada Mane Institute of Technology, Solapur.
3. Orchid College of Engineering & Technology, Solapur.
4. SVERI's College of Engineering, Pandharpur.
It focussed on what 'BigData' is and how the next generation of professionals should be ready the BigData revolution
DevOps and Testing slides at DASA ConnectKari Kakkonen
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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.
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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
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
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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?
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The Art of the Pitch: WordPress Relationships and Sales
Predicting rainfall using ensemble of ensembles
1. Predicting rainfall using ensemble of Ensembles.∗†
Prolok Sundaresan, Varad Meru, and Prateek Jain‡
University of California, Irvine
{sunderap,vmeru,prateekj}@uci.edu
Abstract
Regression is an approach for modeling the relationship between data X
and the dependent variable y. In this report, we present our experiments
with multiple approaches, ranging from Ensemble of Learning to Deep
Learning Networks on the weather modeling data to predict the rainfall.
The competition was held on the online data science competition portal
‘Kaggle’. The results for weighted ensemble of learners gave us a top-10
ranking, with the testing root-mean-squared error being 0.5878.
1 Introduction
The task of this in-class Kaggle competition was to predict the amount of rainfall
at a particular location using satellite data. We wanted to try various algorithms
and ensembles for regression to experiment and learn. The report is structured
in the following manner. The section 2 describes the dataset contents and the
latent structure found using latent variable analysis and clustering. This was
done by Prolok and Prateek. The section 3 describes various models used in
the project in detail. The Neural Network/Deep Learning section was done
by Varad. Random Forests was done by Prolok and Prateek. The work on
Gradient Boosting was done by Prateek and Varad. The section 4 described
the ensemble of ensembles technique used by us. The ensemble sits on top of
different ensembles and learners which were done in section 3. The work on the
final ensemble was done by all the three members. The section 5 presents our
learning and conclusion.
2 Understanding The Data
Visualizing the data was a difficult task since the data was in 91 dimensions.
In order to look for patterns in the data and visualized it, we applied SVD
technique to reduce the dimensionality of the features to 2 principle dimensions.
Then we applied k means clustering with k=5 on the data with 91 dimensions
∗The online competition is available at the Kaggle website https://inclass.kaggle.com/
c/how-s-the-weather. The name of the team was skynet
†This work was does as a part of the project for CS 273: Machine Learning, Fall 2014,
taught by Prof. Alexander Ihler.
‡Prolok Sundaresan: Student# 66008474, Varad Meru: Student# 26648958, Prateek Jain:
Student# 28321844
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2. and plotted the assignments in the 2 dimensional transformed feature space.
We saw patterns in the data. Especially some points were densely clustered and
some were sparse.
To visualize it better, we transformed the feature in 3 dimensional space,
with the first 3 principle components, and saw that the points were clustered
around 3 planes.
Figure 1: Visualizing the data in 3 dimensions
3 Machine Learning Models
3.1 Mixture of Experts
As seen from our visualization in Figure 1, we could identify two highly dense
areas of the feature data on either side of a region of sparsely distributed data.
The idea behind using the mixture of experts approach was, that intuitively, it
would be difficult for a single regressor to fit the dataset, since the distribution
is non-uniform. We decided to split the data into clusters. To cluster the data,
we used several initialization of the k means algorithm with the kmeans++. We
used number of clusters as one of the parameters of our model which we tried
to change.
Since each of the clusters got a subset of a points from the original dataset,
number of data points per cluster was not a very large number. Our concern
with this was that any model we chose would overfit the data in its cluster.
Therefore, we used the ensemble method of gradient boosting for each of the
clusters. Since, in gradient boosting, we start with an underfitting model and
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3. (a) Cluster assignments of Data Points
(b) Mixture of Experts Error
Figure 2: Visualizing the principle components of Data
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4. then gradually add complexity, the chances of overfitting would be less in this
model. We decided to use Decision stumps as our regressors for the boosting
algorithm.
For evaluating the prediction for the validation split and the test data, we
first check which cluster the data point belongs to. We did this, by creating a K
nearest neighbor classifier on the center of the 3 clusters created in the previous
step. Then, the classifier predicts the cluster assignment for each test point,
and we use the array of boosting regressors corresponding to that cluster on the
data point, to get its corresponding prediction.
The parameters of the model we modified were the number of clusters and
the number of regressors used for boosting. We found that though the test error
reduced considerably on increasing the regressors for boosting, the validation
error increased after a certain point as can be seen from Figure 3. We got
minimum validation error for 700 regressors.
3.2 Neural Networks
We implemented various types of neural networks, ranging from single layer
networks to 3-layer sigmoidal neural networks.
Single Layer Network
Figure 3: Single Layer Architecture.
We build the neural network using the MATLAB’s Neural-Network-Toolkit
and PyBrain library implemented in Python. For the MATLAB implementa-
tion, there were various runs made for different number of neurons in the hidden
layer. The architecture of the neural network can be seen in Figure 3. The Fig-
ure 4 show the train-test-validation plots for different network architectures.
The dataset was distributed into 70% (Training), 20% (Validation) and 10%
(Testing) section for the neural network to run. The subsection 3.4 shows the
performance of the models learned. It was seen that the neural networks started
to overfit as the number of neurons were increased more than 40.
# of Neurons Training Error (RMSE) Testing Error (RMSE)
10 0.5986 0.61341
20 0.5875 0.61301
50 0.5852 0.62889
Table 1: RMSE Error rates for different network architectures.
It was observed that the learner could not learn very accurately as the data
a lot as the data was not much for the neural network to learn on.
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5. (a) Train-Validation-Test error plot for 10
neuron hidden layer
(b) Error distribution histogram for 10 neuron
hidden layer
(c) Train-Validation-Test error plot for 20
neuron hidden layer
(d) Error distribution histogram for 20 neuron
hidden layer
(e) Train-Validation-Test error plot for 50
neuron hidden layer
(f) Error distribution histogram for 50 neuron
hidden layer
Figure 4: Plots of various Train-Validation-Test error for number of neurons =
[10, 20, 50]
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6. Deep Networks
For this project, we tried using deep networks as well. The deep network was
made using PyBrain. We tried using different activation functions and archi-
tectures to understand how deep networks would work. The architecture shown
in Figure 5 had 3 layers - visible later contains 91 neurons, the first hidden
layer (tanh) had 91 neurons, the second hidden layer (sigmoid) had 50 neu-
rons, the third hidden layer (sigmoid) had 20 neurons, and the output layer
had 1 linear node. The testing error was 0.83643 was very high compared to
other approaches. We concluded that the network was learning the data well,
but was overfitting.
Input
layer
Hidden layer
(Hyperbolic
Tangent)
Hidden
layer(Sigmoid)
y1
y2
y3
Output
layer
3.3 Gradient Boosting
In parallel, we worked on training the gradient boosting model with varying
parameters to get the best fit for the data. We started with basic decision
stumps with number of regressors ranging from 1 to 2000. We also varied the
maximum Depth for the decision tree used as the regression model from 3 to 7.
We used alpha 0.9 for our algorithm. We observed that we got best performance
with 2000 boosters and depth as 7.
3.4 Random Forests
Several aspects of Random Forest technique was explored. The major funda-
mental behind Random Forest is to take a model, that overfits, the data, then
use feature and data bagging to bring down the complexity to fit the data bet-
ter. The usual model that is used in Random Forest is a high depth Regression
tree. We tried to explore other models, that overfitted the data.
The first option was to consider simple linear regression with feature trans-
formation. The data from X1 was transformed into X1 and X12
features and
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7. Figure 5: Train and Test error plot for Gradient Boosting vs number of learners
linear regression was done on that. Significantly better results were obtained in
this transformation( a test error of 0.4322 compared to 0.4181) , but it signifi-
cantly worsened with an addition of X13
features to the feature list. This was
used as the regressor for the Random Forests, but the results were better for
a Tree Regressor. The major take away from this analysis was the use of X22
features into the feature list for tree regression. Several other regressors were
also tried like knn regressor was used, but tree regressor came out on top.
Since Decision Tree regression was significantly better than linear regression
in Random Forest, we decided to proceed with that with the X22
features also
in place(a total of 182 features). nFeatures was chosen as 150, and the depth was
set as 13,14,15,16,17, of which a maxDepth of 14 obtained optimal performance.
150 decision trees were learned and the optimum results were obtained for 90
learners.
Learner Training Error (MSE) Testing Error (MSE)
Linear Regressor 0.4068 0.4243
Linear Regressor with X12
feature 0.3996 0.4140
Tree Regressor 0.1951 0.3822
Table 2: MSE Error rates for Random Forests
4 Ensemble of all Learners
At the end, since we trained a lot of learners separately, some of which were
ensembles themselves, we thought of aggregating the results of the learners
to improve our prediction.We also analyzed the variance between the results
of our learners, and an average variance of 0.0204 was obtained. Since the
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8. variance was noticeable, a weighted average aggregation of the results seemed
the best approach. We chose the model parameters for the best performing
models from each category to get a consolidated result. The section 4 shows
the architecture of our ensember. Initially, we chose a very simple approach of
assigning all models with the same weights to get a prediction. We got a some
improvement with MSE of 0.5908. We, saw that this was performing just below
our best individual prediction model. So, we decided to bump the weight of our
best learner in the ensemble. This helped improve our accumulated prediction,
providing an MSE of 0.5878.
Figure 6: Ensemble of Learners
5 Conclusion
This project gave a us glimpse on how machine learning techniques are applied
to real world problems. We applied a variety of techniques including neural
networks, decision trees, random forests, gradient boosting, kmeans clustering,
and PCA. Testing out various parameters of the different learner types helped us
identify where each of the models under-fitted and over-fitted the data. Finally,
while modifying the parameters of each model helped us reduce the variance in
the models, we used a final weighted ensemble of various learners to reduce the
bias of individual learners.
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