DOCSLIB.ORG

Program Guide April 5, 2016

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Program Guide April 5, 2016 PROGRAM GUIDE APRIL 5, 2016 Quest A forum for student scholarship 1 2 FORWARD We sincerely congratulate each of the student presenters and faculty mentors for QUEST: A Forum for Student Scholarship. This year’s program includes more than 370 student participants. Now in its 27th year, QUEST has become a Youngstown State University tradition of excellence in academic achievement, innovation, discovery, and scholarship. QUEST recognizes the outstanding scholarly achievements by our outstanding YSU students, working in collaboration with equally outstanding faculty mentors The QUEST committee and University administration are grateful for the guidance and commitment of Youngstown State University’s dedicated faculty in providing inspiration, motivation, and support for their students and making these presentations possible. Many of these presentations represent scholarly endeavors made possible through individual faculty grants, as well as through support from the Office of Research University Research Council grants. We encourage QUEST participants to actively engage in and appreciate the scholarly activities of one other, seeking to discover new knowledge within those exciting interdisciplinary collaborations. In addition to serving as a forum which summarizes prior success, we hope that QUEST serves as a starting point for new partnerships, collaborations, and discovery amongst students and faculty from all of our academic disciplines. Michael A. Hripko Associate Vice President for Research Youngstown State University 3 ACKNOWLEDGEMENTS We appreciate the ongoing support for QUEST 2016 from the following individuals, organizations, and programs: YSU Board of Trustees YSU Faculty YSU President, Mr. James P. Tressel YSU Provost Dr. Martin A. Abraham YSU Foundation, Mr. Paul McFadden, President YSU Honors College Students and Director, Dr. Amy Cossentino Undergraduate Research Council (URC) Grant Program College of Liberal Arts and Social Sciences Beeghly College of Education Bitonte College Of Health And Human Services College Of Creative Arts and Communications College Of Science, Technology, Engineering and Mathematics Williamson College of Business Administration Kilcawley Center Staff YSU Office of Marketing Communications Chartwells Special Thanks to Dr. Jeff Coldren for his ongoing leadership and efforts in support of QUEST. 4 QUEST 2016: A Forum for Student Scholarship Sessions, Topics, and Rooms 8:30 – 10:00 – Session 1a – Posters (Biology) – Ohio Room 8:30 – 10:00 – Session 1b – Posters (Electrical Engineering) – Bresnahan 1 & 2 8:30 – 10:00 – Session 2 – Oral Presentations Environmental Studies - Coffelt Engineering (Graduate) - Jones STEM - Humphrey Computer Science & Mechanical Engineering I - James Gallery 10:30 – 12:00 – Session 3a – Posters (COFSP - Seniors) – Ohio Room 10:30 – 12:00– Session 3b – Posters (COFSP) – Bresnahan 1 & 2 10:30 – 12:00 – Session 4 – Oral Presentations Business – Coffelt Psychology – Jones Respiratory Care I – Humphrey Mechanical Engineering – James Gallery 12:00 – 1:15 – Luncheon & Keynote Speaker – Chestnut Room 1:30 – 3:00 - Session 5a – Posters (Chemistry) – Ohio Room 1:30 – 3:00 – Session 5b – Posters (STEM) – Bresnahan 1 & 2 1:30 – 3:00 – Session 6 – Oral Presentations Communication, Counseling, & Education - Coffelt Anthropology & Geography - Jones Respiratory Care II – Humphrey Electrical Engineering I – James Gallery 3:30 – 5:00 – Session 7a – Posters (HHS) – Ohio Room 3:30 – 5:00 – Session 7b – Posters (Criminal Justice) – Bresnahan 1 & 2 3:30 – 5:00 - Session 8 - Oral Presentations Writing & Ethnicity - Coffelt Dental Hygiene – Jones Music – Humphrey Electrical Engineering II – James Gallery 5 QUEST 2016: A Forum for Student Scholarship Projects organized by sessions and topics Session 1a 8:30 -10:00 Biology Room: Ohio Poster Protein Expression of C2C12 Cells (Project ID: 151) Allison Guerrieri, Catherine Grabowski, Coral Li Biochemistry Faculty sponsor(s): Gary R. Walker Session 1a 8:30 -10:00 Biology Room: Ohio Poster A Water Quality Analysis of Costa Rica’s Sarapiqui and Puerto Viejo Rivers (Project ID: 100) Austin Cunningham, Mike Dercoli, Taryn Hanna, Isaac Pearce, and Joanna Schnell. Biology Faculty sponsor(s): Carl G. Johnston Session 1a 8:30 -10:00 Biology Room: Ohio Poster Identifying peptides that bind to Human Serum Albumin using Phage Display for the development of sensors that detect injury in military personnel. (Project ID: 51) Bill Rees, George Kubas Biology Faculty sponsor(s): Diana L. Fagan Session 1a 8:30 -10:00 Biology Room: Ohio Poster Histological Analysis of Surgical Wound Healing (Project ID: 334) Coral Z. Li, Marta Burak Biology Faculty sponsor(s): Mark D. Womble Session 1a 8:30 -10:00 Biology Room: Ohio Poster Induction of the QA-Y and QA-1F Genes in Neurospora crassa at Differing Times of Quinic Acid Exposure (Project ID: 142) Kory George Biology Faculty sponsor(s): David K. Asch Session 1a 8:30 -10:00 Biology Room: Ohio Poster The Effect of Soothing Audio or Visual Stimuli on the Galvanic Skin Response (Project ID: 362) Michaela Randall, Hannah Grimes, Carmen Moradian, Alexa Schmidt Biology Faculty sponsor(s): Johanna Krontiris-Litowitz 6 Session 1a 8:30 -10:00 Biology Room: Ohio Poster Quantitation and Characterization of Collagen Oxidation in TGF- Beta Stimulated Fibroblast Cultures (Project ID: 98) Muhammad Erfan Uddin, Trisha Krstinovski Biology Faculty sponsor(s): Johanna Krontiris-Litowitz Session 1a 8:30 -10:00 Biology Room: Ohio Poster Phage Display Technology to Identify a Peptide Ligand to Bind to Type 8 Capsular Polysaccharide of Staphylococcus aureus (Project ID: 136) Nina Lenkey, Joshua Fowler Biology Faculty sponsor(s): Diana L. Fagan Session 1a 8:30 -10:00 Biology Room: Ohio Poster A Long Term Care Emergency Preparedness Planning Analysis of Mahoning County (Project ID: 345) Rachelle Fair, Michael Buchenic Biology Pre Medical Faculty sponsor(s): Daniel J. Van Dussen Session 1a 8:30 -10:00 Biology Room: Ohio Poster Antibody Production Directed Against Type 5 and Type 8 Staphylococcus aureus (Project ID: 370) Rayann Atway Biology Faculty sponsor(s): Diana L. Fagan Session 1a 8:30 -10:00 Biology Room: Ohio Poster Investigation of Peroxidase Oxidation of Human Fibroblast Collagen (Project ID: 356) Trisha Krstinovski, Muhammad Erfan Uddin Biology Pre Medical Faculty sponsor(s): Johanna Krontiris-Litowitz Session 1a 8:30 -10:00 Biology Room: Ohio Poster Phytoremediation of Lead Contaminated Soil from an Abandoned Urban Lot (Project ID: 204) Yener Ulus Biology Faculty sponsor(s): Carl G. Johnston 7 Session 1a 8:30 -10:00 Biology Room: Ohio Poster Restoration of Melanin in Albino Mutant of Fonseace monophora can Reverse Its Pathogenicity to the Heterologous Insect Host Galleria mellonella (Project ID: 106) Yinghui Liu Biology Faculty sponsor(s): Chester R. Cooper, Liyan Xi Session 1a 8:30 -10:00 Biology Room: Ohio Poster Regulation of Cell-Wall Biosynthesis by the yakA Gene of the Dimoprhic, Pathogenic Fungus Penicillium marneffei (Project ID: 107) Zackery Zuga, Sarah Eisnaugle, Josh Engle, Sumanun Suwunnakorn Biology Faculty sponsor(s): Chester R. Cooper, Nongnuch Vanittanakom Session 1a 8:30 -10:00 Biology Room: Ohio Poster Effects of fluoride on pineal morphology in rats (Project ID: 387) Aaron Mrvelj Biology Faculty sponsor(s): Mark Womble Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster RC Pathfinder (Project ID: 265) Bradlee Roberts, Stephen Schlitt, William Santos Elect Engr Comp Digital Opt Faculty sponsor(s): Jalal Jalali Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster Locomotive Control Board (Project ID: 180) Christopher Poullas, Stuart Blank, Thomas Hornung Engineering Electrical Faculty sponsor(s): Jalal Jalali Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster Image Recognition Protection System (Project ID: 154) Karan Eunni, Nicolette Allen, Nathan Niemi Electrical Engineering Faculty sponsor(s): Jalal Jalali 8 Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster The Digital Theremin (Project ID: 188) Ryan Thomas, Donald Koehler, Ahmed Alghamdi Elect Engr Comp Digital Opt Faculty sponsor(s): Jalal Jalali Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster Modcopter (Project ID: 8) Stephen Koulianos, Garrett McIntyre, James Carter, Lucas Ciprian, Cory Thomas Elect Engr Comp Digital Opt Faculty sponsor(s): Jalal Jalali Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster Home Automation System (Project ID: 148) Sultan Almujil. Ali Dhiaaaldeen, Abdulrhman Alshahrani Electrical Engineering Faculty sponsor(s): Jalal Jalali Session 1b 8:30 -10:00 Electrical Engineering Room: Bresnahan 1 & 2 Poster Robotic Packing (Project ID: 344) Xinyuan Li Electrical Engineering Faculty sponsor(s): Jalal Jalali Session 2 8:30 - 10:00 Environmental Studies Room: Coffelt Oral Overall Nutrient Removal from a Constructed Wetland into a Pond Water System (Project ID: 7) Charles Dawson, Katherine Pitcairn Environmental Studies Faculty sponsor(s): Felicia P. Armstrong Session 2 8:30 - 10:00 Environmental Studies Room: Coffelt Oral Comparing Methods of Soil Particle Size Analysis (Project ID: 366) Jacon Taylor Environmental Studies Faculty sponsor(s): Felicia P. Armstrong Session 2 8:30 - 10:00 Environmental Studies Room: Coffelt Oral Assessing diatom community response to seasonal environmental change in Meander Creek Reservoir (Project ID: 310) Jessica Crawford Environmental
Load more

Recommended publications
  • Naiad: a Timely Dataflow System
    Naiad: A Timely Dataflow System Derek G. Murray Frank McSherry Rebecca Isaacs Michael Isard Paul Barham Mart´ın Abadi Microsoft Research Silicon Valley {derekmur,mcsherry,risaacs,misard,pbar,abadi}@microsoft.com Abstract User queries Low-latency query are received responses are delivered Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput Queries are of batch processors, the low latency of stream proces- joined with sors, and the ability to perform iterative and incremental processed data computations. Although existing systems offer some of Complex processing these features, applications that require all three have re- incrementally re- lied on multiple platforms, at the expense of efficiency, Updates to executes to reflect maintainability, and simplicity. Naiad resolves the com- data arrive changed data plexities of combining these features in one framework. A new computational model, timely dataflow, under- Figure 1: A Naiad application that supports real- lies Naiad and captures opportunities for parallelism time queries on continually updated data. The across a wide class of algorithms. This model enriches dashed rectangle represents iterative processing that dataflow computation with timestamps that represent incrementally updates as new data arrive. logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. requirements: the application performs iterative process- We show that many powerful high-level programming ing on a real-time data stream, and supports interac- models can be built on Naiad’s low-level primitives, en- tive queries on a fresh, consistent view of the results. abling such diverse tasks as streaming data analysis, it- However, no existing system satisfies all three require- erative machine learning, and interactive graph mining.
    [Show full text]
  • ML Cheatsheet Documentation
    ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions.
    [Show full text]
  • 1.2 Le Zhang, Microsoft
    Objective • “Taking recommendation technology to the masses” • Helping researchers and developers to quickly select, prototype, demonstrate, and productionize a recommender system • Accelerating enterprise-grade development and deployment of a recommender system into production • Key takeaways of the talk • Systematic overview of the recommendation technology from a pragmatic perspective • Best practices (with example codes) in developing recommender systems • State-of-the-art academic research in recommendation algorithms Outline • Recommendation system in modern business (10min) • Recommendation algorithms and implementations (20min) • End to end example of building a scalable recommender (10min) • Q & A (5min) Recommendation system in modern business “35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from recommendations algorithms” McKinsey & Co Challenges Limited resource Fragmented solutions Fast-growing area New algorithms sprout There is limited reference every day – not many Packages/tools/modules off- and guidance to build a people have such the-shelf are very recommender system on expertise to implement fragmented, not scalable, scale to support and deploy a and not well compatible with enterprise-grade recommender by using each other scenarios the state-of-the-arts algorithms Microsoft/Recommenders • Microsoft/Recommenders • Collaborative development efforts of Microsoft Cloud & AI data scientists, Microsoft Research researchers, academia researchers, etc. • Github url: https://github.com/Microsoft/Recommenders • Contents • Utilities: modular functions for model creation, data manipulation, evaluation, etc. • Algorithms: SVD, SAR, ALS, NCF, Wide&Deep, xDeepFM, DKN, etc. • Notebooks: HOW-TO examples for end to end recommender building. • Highlights • 3700+ stars on GitHub • Featured in YC Hacker News, O’Reily Data Newsletter, GitHub weekly trending list, etc.
    [Show full text]
  • Alibaba Amazon
    Online Virtual Sponsor Expo Sunday December 6th Amazon | Science Alibaba Neural Magic Facebook Scale AI IBM Sony Apple Quantum Black Benevolent AI Zalando Kuaishou Cruise Ant Group | Alipay Microsoft Wild Me Deep Genomics Netflix Research Google Research CausaLens Hudson River Trading 1 TALKS & PANELS Scikit-learn and Fairness, Tools and Challenges 5 AM PST (1 PM UTC) Speaker: Adrin Jalali Fairness, accountability, and transparency in machine learning have become a major part of the ML discourse. Since these issues have attracted attention from the public, and certain legislations are being put in place regulating the usage of machine learning in certain domains, the industry has been catching up with the topic and a few groups have been developing toolboxes to allow practitioners incorporate fairness constraints into their pipelines and make their models more transparent and accountable. AIF360 and fairlearn are just two examples available in Python. On the machine learning side, scikit-learn has been one of the most commonly used libraries which has been extended by third party libraries such as XGBoost and imbalanced-learn. However, when it comes to incorporating fairness constraints in a usual scikit- learn pipeline, there are challenges and limitations related to the API, which has made developing a scikit-learn compatible fairness focused package challenging and hampering the adoption of these tools in the industry. In this talk, we start with a common classification pipeline, then we assess fairness/bias of the data/outputs using disparate impact ratio as an example metric, and finally mitigate the unfair outputs and search for hyperparameters which give the best accuracy while satisfying fairness constraints.
    [Show full text]
  • Microsoft Recommenders Tools to Accelerate Developing Recommender Systems Scott Graham, Jun Ki Min, Tao Wu {Scott.Graham, Jun.Min, Tao.Wu} @ Microsoft.Com
    Microsoft Recommenders Tools to Accelerate Developing Recommender Systems Scott Graham, Jun Ki Min, Tao Wu {Scott.Graham, Jun.Min, Tao.Wu} @ microsoft.com Microsoft Recommenders repository is an open source collection of python utilities and Jupyter notebooks to help accelerate the process of designing, evaluating, and deploying recommender systems. The repository is actively maintained and constantly being improved and extended. It is publicly available on GitHub and licensed through a permissive MIT License to promote widespread use of the tools. Core components reco_utils • Dataset helpers and splitters • Ranking & rating metrics • Hyperparameter tuning helpers • Operationalization utilities notebooks • Spark ALS • Inhouse: SAR, Vowpal Wabbit, LightGBM, RLRMC, xDeepFM • Deep learning: NCF, Wide-Deep • Open source frameworks: Surprise, FastAI tests • Unit tests • Smoke & Integration tests Workflow Train set Recommender train Environment Data Data split setup load Validation set Hyperparameter tune Operationalize Test set Evaluate • Rating metrics • Content based models • Clone & conda-install on a local / • Chronological split • Ranking metrics • Collaborative-filtering models virtual machine (Windows / Linux / • Stratified split • Classification metrics • Deep-learning models Mac) or Databricks • Non-overlap split • Docker • Azure Machine Learning service (AzureML) • Random split • Tuning libraries (NNI, HyperOpt) • pip install • Azure Kubernetes Service (AKS) • Tuning services (AzureML) • Azure Databricks Example: Movie Recommendation
    [Show full text]
  • The Design and Implementation of Low-Latency Prediction Serving Systems
    The Design and Implementation of Low-Latency Prediction Serving Systems Daniel Crankshaw Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2019-171 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-171.html December 16, 2019 Copyright © 2019, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement To my parents The Design and Implementation of Low-Latency Prediction Serving Systems by Daniel Crankshaw A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Joseph Gonzalez, Chair Professor Ion Stoica Professor Fernando Perez Fall 2019 The Design and Implementation of Low-Latency Prediction Serving Systems Copyright 2019 by Daniel Crankshaw 1 Abstract The Design and Implementation of Low-Latency Prediction Serving Systems by Daniel Crankshaw Doctor of Philosophy in Computer Science University of California, Berkeley Professor Joseph Gonzalez, Chair Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application.
    [Show full text]
  • With Honors Final Year Peoject 1
    FACULTY OF INFORMATICS AND COMPUTING BACHELOR OF COMPUTER SCIENCE (COMPUTER NETWORK SECURITY) WITH HONORS FINAL YEAR PEOJECT 1 CSF 35104 3RD YEAR 5TH SEMESTER SESSION 2018/2019 SPAM DETECTION USING MACHINE LEARNING BASED BINARY CLASSIFIER PREPARED BY : NOR SYAIDATUL AMIRAH BINTI ABDUL HAMID MATRIC NUMBER : BTBL16043660 SUPERVISOR : MR. AHMAD FAISAL AMRI BIN ABIDIN@BHARUN CHAPTER 1 INTRODUCTION 1.1 BACKGROUND Today, spam has become a big trouble over the internet. Up to 2017, the statistic shown spam accounted for 55%of all e-mail messages, same as during the previous year [1]. Spam which is also known as unsolicited bulk email has led to the increasing use of email as email provides the perfect ways to send the unwanted advertisement or junk newsgroup posting at no cost for the sender. This chances has been extensively exploited by irresponsible organizations and resulting to clutter the mail boxes of millions of people all around the world. Evolving from a minor to major concern, given the high offensive content of messages, spam is a waste of time. It also consumed a lot of storage space and communication bandwidth. End user is at risk of deleting legitimate mail by mistake. Moreover, spam also impacted the economical which led some countries to adopt legislation. Text classification is used to determine the path of incoming mail/message either into inbox or straight to spam folder. It is the process of assigning categories to text according to its content. It is used to organized, structures and categorize text. It can be done either manually or automatically. Machine learning automatically classifies the text in a much faster way than manual technique.
    [Show full text]
  • Anna Choromanska
    ANNA CHOROMANSKA CONTACT Department of Electrical and Computer Engineering NYU Tandon School of Engineering New York University Room LC266D, 5 Metrotech Center, New York, NY 11201, USA E-mail: ac5455 at nyu dot edu, achoroma at gmail dot com Website: https://engineering.nyu.edu/faculty/anna-choromanska EDUCATION • Columbia University in the City of New York, Department of Electrical Engineering, M.Phil. and Ph.D. (with Presidential Fellowship), 2009-2014 (Advisor: Prof. Tony Jebara, Co-advisor: Prof. Claire Monteleoni) • Warsaw University of Technology, Department of Electronics and Information Technology, MSc with distinctions (double specialization: Electronics and Computer Engineering and Electron- ics and Informatics in Medicine), 2004-2009 (Advisor: Prof. Antoni Grzanka) • Mieczyslaw Karlowicz Music High School in Warsaw, Department of Piano Play, 1998-2004 PROFESSIONAL EXPERIENCE Assistant Professor Department of Electrical and Computer 01.2017 now Engineering, NYU Tandon School of Engineering Affiliated Faculty Member NYU Center for Urban Science 02.2019 now and Progress Affiliated Faculty Member NYU Center for Data Science 01.2017 now ||||||||||||||||||||||||||||||||||||||||||| Research Collaboration NYU - NVIDIA AI Labs (NVAIL) 02.2019 now Working on deep-learning-based AI platforms for autonomous driving. Research Collaboration IBM T. J. Watson Research Center 08.2017 now Working on biologically plausible algorithms for training deep networks. Research Collaboration NVIDIA (New Jersey lab) 05.2016 now Working on autonomous car driving. Post-Doctoral Associate Computer Science Department, 04.2014 12.2016 Courant Institute of Mathematical Sciences, New York University Working on deep learning (advisor: Prof. Yann LeCun). Summer Internship Microsoft Research, New York 06.2013 09.2013 Research Collaboration 09.2013 06.2014 Working on logarithmic time extreme multiclass classification (advisor: Dr John Langford).
    [Show full text]
  • Extreme Multi-Label Classification for Information Retrieval XMLC4IR
    Extreme Multi-label Classification for Information Retrieval XMLC4IR Tutorial at ECIR 2018 Rohit Babbar1 and Krzysztof Dembczy´nski2 1Aalto University, Finland, 2Pozna´nUniversity of Technology, Poland European Conference on Information Retrieval Grenoble, France, March 26, 2018 • Rohit Babbar: I Affiliation: University Aalto I Previous affiliations: Max-Planck Institute for Intelligent Systems, Universit´eGrenoble Alpes I Main interests: extreme classification, multi-label classification, multi-class classification, text classification 1 / 94 • Krzysztof Dembczy´nski: I Affiliation: Poznan University of Technology I Previous affiliations: Marburg University I Main interests: extreme multi-machine learning, multi-label classification, label tree algorithms, learning theory 2 / 94 Agenda 1 Extreme classification: applications and challenges 2 Algorithms I Label embeddings I Smart 1-vs-All approaches I Tree-based methods I Label filtering/maximum inner product search 3 Live demonstration Webpage: http://www.cs.put.poznan.pl/ kdembczynski/xmlc-tutorial-ecir-2018/ 3 / 94 Agenda 1 Extreme classification: applications and challenges 2 Algorithms I Label embeddings I Smart 1-vs-All approaches I Tree-based methods I Label filtering/maximum inner product search 3 Live demonstration Webpage: http://www.cs.put.poznan.pl/ kdembczynski/xmlc-tutorial-ecir-2018/ 3 / 94 Extreme multi-label classification is a problem of labeling an item with a small set of tags out of an extremely large number of potential tags. 4 / 94 Geoff Hinton Andrew Ng Yann LeCun Yoshua Bengio 5 /
    [Show full text]
  • Analytical and Other Software on the Secure Research Environment
    Analytical and other software on the Secure Research Environment The Research Environment runs with the operating system: Microsoft Windows Server 2016 DataCenter edition. The environment is based on the Microsoft Data Science virtual machine template and includes the following software: • R Version 4.0.2 (2020-06-22), as part of Microsoft R Open • R Studio Desktop 1.3.1093 working with R 4.0.2 • Anaconda 3, including an environment for Python 3.8.5 • Python, 3.8.5 as part of the Anaconda base environment • Jupyter Notebook, as part of the Anaconda3 environment • Microsoft Office 2016 Standard edition, including Word, Excel, PowerPoint, and OneNote (Access not included) • JuliaPro 0.5.1.1 and the Juno IDE for Julia • PyCharm Community Edition, 2020.3 • PLINK • JAGS • WinBUGS • OpenBUGS • stan and rstan • Apache Spark 2.2.0 • SparkML and pySpark • Apache Drill 1.11.0 • MAPR Drill driver • VIM 8.0.606 • TensorFlow • MXNet, MXNet Model Server • Microsoft Cognitive Toolkit (CNTK) • Weka • Vowpal Wabbit • xgboost • Team Data Science Process (TDSP) Utilities • VOTT (Visual Object Tagging Tool) 1.6.11 • Microsoft Machine Learning Server • PowerBI • Docker version 10.03.5, build 2ee0c57608 • SQL Server Developer Edition (2017), including Management Studio and SQL Server Integration Services (SSIS) • Visual Studio Code 1.17.1 • Nodejs • 7-zip • Evince PDF Viewer • Acrobat Reader • Microsoft Photo Viewer • PowerShell 6 March 2021 Version 1.2 And in the Premium research environments: • STATA 16.1 • SAS 9.4, m4 (academic license) Users also have the ability to bring in additional software if the software was specified in the data request, the software runs in the operating system described above, and the user can provide Vivli with any necessary licensing keys.
    [Show full text]
  • Azureml: Anatomy of a Machine Learning Service
    JMLR: Workshop and Conference Proceedings 50:1–13, 2016 PAPIs 2015 AzureML: Anatomy of a machine learning service AzureML Team, Microsoft ∗ 1 Memorial Drive, Cambridge, MA 02142 Editor: Louis Dorard, Mark D. Reid and Francisco J. Martin Abstract In this paper, we describe AzureML, a web service that provides a model authoring en- vironment where data scientists can create machine learning models and publish them easily (http://www.azure.com/ml). In addition, AzureML provides several distinguishing features. These include: (a) collaboration, (b) versioning, (c) visual workflows, (d) exter- nal language support, (e) push-button operationalization, (f) monetization and (g) service tiers. We outline the system overview, design principles and lessons learned in building such a system. Keywords: Machine learning, predictive API 1. Introduction With the rise of big-data, machine learning has moved from being an academic interest to providing competitive advantage to data driven companies. Previously, building such predic- tive systems required deep expertise in machine learning as well as scalable software engineer- ing. Recent trends have democratized machine learning and made building predictive appli- cations far easier. Firstly, the emergence of machine learning libraries such as scikit-learn Pe- dregosa et al.(2011), WEKA Holmes et al.(1994), Vowpal Wabbit Langford et al.(2007) etc. and open source languages like R R Development Core Team(2011) and Julia Bezanson et al. (2012) have made building machine learning models easier even without machine learning expertise. Secondly, scalable data frameworks Zaharia et al.(2010); Low et al.(2010) have provided necessary infrastructure for handling large amounts of data. Operationalizing mod- els built this way still remains a challenge.
    [Show full text]
  • Package 'Rvowpalwabbit'
    Package ‘RVowpalWabbit’ August 7, 2020 Title R Interface to the Vowpal Wabbit Version 0.0.15 Date 2020-08-03 Maintainer Dirk Eddelbuettel <[email protected]> Author Dirk Eddelbuettel Description The 'Vowpal Wabbit' project is a fast out-of-core learning system sponsored by Microsoft Research (having started at Yahoo! Research) and written by John Langford along with a number of contributors. This R package does not include the distributed computing implementation of the cluster/ directory of the upstream sources. Use of the software as a network service is also not directly supported as the aim is a simpler direct call from R for validation and comparison. Note that this package contains an embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub repo <https://github.com/eddelbuettel/rvw> can provide an alternative using an external 'Vowpal Wabbit' library installation. Depends R (>= 2.12.0) Imports Rcpp LinkingTo Rcpp SystemRequirements The Boost 'program_options' library <https://boost.org> is required. OS_type unix StagedInstall no License GPL (>= 2) URL https://vowpalwabbit.org/ BugReports https://github.com/eddelbuettel/rvowpalwabbit/issues NeedsCompilation yes Repository CRAN Date/Publication 2020-08-07 08:40:15 UTC 1 2 vw R topics documented: vw..............................................2 Index 4 vw Run the Vowpal Wabbit fast out-of-core learner Description The vw function applies the Vowpal Wabbit on-line learner to a given data set and model. Vowpal Wabbit is a project sponsored by Yahoo! Research and led by John Langford. At present, this package provides a simple yet crude interface. Usage vw(args, quiet=TRUE) Arguments args A character vector containing the same arguments one would use on the command- line with the standalone vw binary.
    [Show full text]