Reverse Image Search for Scientific Data Within and Beyond the Visible
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Asset Finder: a Search Tool for Finding Relevant Graphical Assets Using Automated Image Labelling
Sjalvst¨ andigt¨ arbete i informationsteknologi 20 juni 2019 Asset Finder: A Search Tool for Finding Relevant Graphical Assets Using Automated Image Labelling Max Perea During¨ Anton Gildebrand Markus Linghede Civilingenjorsprogrammet¨ i informationsteknologi Master Programme in Computer and Information Engineering Abstract Institutionen for¨ Asset Finder: A Search Tool for Finding Rele- informationsteknologi vant Graphical Assets Using Automated Image Besoksadress:¨ Labelling ITC, Polacksbacken Lagerhyddsv¨ agen¨ 2 Max Perea During¨ Postadress: Box 337 Anton Gildebrand 751 05 Uppsala Markus Linghede Hemsida: http:/www.it.uu.se The creation of digital 3D-environments appears in a variety of contexts such as movie making, video game development, advertising, architec- ture, infrastructure planning and education. When creating these envi- ronments it is sometimes necessary to search for graphical assets in big digital libraries by trying different search terms. The goal of this project is to provide an alternative way to find graphical assets by creating a tool called Asset Finder that allows the user to search using images in- stead of words. The Asset Finder uses image labelling provided by the Google Vision API to find relevant search terms. The tool then uses synonyms and related words to increase the amount of search terms us- ing the WordNet database. Finally the results are presented in order of relevance using a score system. The tool is a web application with an in- terface that is easy to use. The results of this project show an application that is able to achieve good results in some of the test cases. Extern handledare: Teddy Bergsman Lind, Quixel AB Handledare: Mats Daniels, Bjorn¨ Victor Examinator: Bjorn¨ Victor Sammanfattning Skapande av 3D-miljoer¨ dyker upp i en mangd¨ olika kontexter som till exempel film- skapande, spelutveckling, reklam, arkitektur, infrastrukturplanering och utbildning. -
Search, Analyse, Predict Image Spread on Twitter
Image Search for Improved Law and Order: Search, Analyse, Predict image spread on Twitter Student Name: Sonal Goel IIIT-D-MTech-CS-GEN-16-MT14026 April, 2016 Indraprastha Institute of Information Technology New Delhi Thesis Committee Dr. Ponnurangam Kumaraguru (Chair) Dr. AV Subramanyam Dr. Samarth Bharadwaj Submitted in partial fulfillment of the requirements for the Degree of M.Tech. in Computer Science, in General Category ©2016 IIIT-D-MTech-CS-GEN-16-MT14026 All rights reserved Keywords: Image Search, Image Virality, Twitter, Law and Order, Police Certificate This is to certify that the thesis titled “Image Search for Improved Law and Order:Search, Analyse, Predict Image Spread on Twitter" submitted by Sonal Goel for the partial ful- fillment of the requirements for the degree of Master of Technology in Computer Science & Engineering is a record of the bonafide work carried out by her under our guidance and super- vision in the Security and Privacy group at Indraprastha Institute of Information Technology, Delhi. This work has not been submitted anywhere else for the reward of any other degree. Dr. Ponnurangam Kumarguru Indraprastha Institute of Information Technology, New Delhi Abstract Social media is often used to spread images that can instigate anger among people, hurt their religious, political, caste, and other sentiments, which in turn can create law and order situation in society. This results in the need for Law Enforcement Agencies (LEA) to inspect the spread of images related to such events on social media in real time. To help LEA analyse the image spread on microblogging websites like Twitter, we developed an Open Source Real Time Image Search System, where the user can give an image, and a supportive text related to image and the system finds the images that are similar to the input image along with their occurrences. -
Neural Network FAQ, Part 1 of 7
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
From Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
I Am Robot: (Deep) Learning to Break Semantic Image Captchas
I Am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs Suphannee Sivakorn, Iasonas Polakis and Angelos D. Keromytis Department of Computer Science Columbia University, New York, USA fsuphannee, polakis, [email protected] Abstract—Since their inception, captchas have been widely accounts and posting of messages in popular services. Ac- used for preventing fraudsters from performing illicit actions. cording to reports, users solve over 200 million reCaptcha Nevertheless, economic incentives have resulted in an arms challenges every day [2]. However, it is widely accepted race, where fraudsters develop automated solvers and, in turn, that users consider captchas to be a nuisance, and may captcha services tweak their design to break the solvers. Recent require multiple attempts before passing a challenge. Even work, however, presented a generic attack that can be applied simple challenges deter a significant amount of users from to any text-based captcha scheme. Fittingly, Google recently visiting a website [3], [4]. Thus, it is imperative to render unveiled the latest version of reCaptcha. The goal of their new the process of solving a captcha challenge as effortless system is twofold; to minimize the effort for legitimate users, as possible for legitimate users, while remaining robust while requiring tasks that are more challenging to computers against automated solvers. Traditionally, automated solvers than text recognition. ReCaptcha is driven by an “advanced have been considered less lucrative than human solvers risk analysis system” that evaluates requests and selects the for underground markets [5], due to the quick response difficulty of the captcha that will be returned. Users may times exhibited by captcha services in tweaking their design. -
Unconventional Computing Using Memristive Nanodevices: from Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
Neural Network FAQ, Part 1 of 7: Introduction
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
A Deep Learning Based CAPTCHA Solver for Vulnerability Assessment
Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment Zahra Noury ∗, Mahdi Rezaei y Faculty of Computer and Electrical Engineering, Qazvin Azad University Faculty of Environment, Institute for Transport Studies, The University of Leeds ∗[email protected], [email protected] Abstract— CAPTCHA is a human-centred test to distinguish a human operator from bots, attacking programs, or other computerised agents that tries to imitate human intelligence. In this research, we investigate a way to crack visual CAPTCHA tests by an automated deep learning based solution. The goal of this research is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator systems; hence, developing more robust CAPTCHAs, without taking the risks of manual try and fail efforts. We develop a Convolutional Neural Network called Deep- CAPTCHA to achieve this goal. The proposed platform is able to investigate both numerical and alphanumerical CAPTCHAs. To train and develop an efficient model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues. Our network’s cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets, respectively. That means more works is required to develop robust CAPTCHAs, to be non- crackable against automated artificial agents. As the outcome of this research, we identify some efficient techniques to improve the security of the CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA model. -
Neural Networks
SLAC-TN-03-028 Neural Networks Patrick Smith Office of Science, Student UndergraduateLaboratory Internship (SULI) Stanford University Stanford Linear Accelerator Center Menlo Park, California August 14,2003 Preparedin partial fulfillment of the requirementsof the Office of Science,Department of Energy’s ScienceUndergraduate Laboratory Internship under the direction of Tony Johnson. Participant: Signature ResearchAdvisor: Signature Work supported in part by the Department of Energy contract DE-AC03-76SF00515. INTRODUCTION Physicistsuse large detectorsto measureparticles createdin high-energy collisions at particle accelerators.These detectors typically producesignals indicating either where ionization occurs along the path of the particle, or where energyis depositedby the.particle.The data producedby thesesignals is fed into pattern recognition programsto try to identify what particles were produced,and to measurethe energy and direction of theseparticles. Ideally, there are many techniquesused in this pattern recognition software. One technique,neural networks, is particularly suitable for identifying what type of particle causedby a set of energy deposits. Neural networks can derive meaningfrom complicatedor imprecisedata, extract patterns,and detect trendsthat are too complex to be noticed by either humansor other computer related processes. To assistin the advancementof this technology,Physicists use a tool kit to experimentwith severalneural network techniques.The goal of this researchis interface a neural network tool kit into JavaAnalysis Studio (JAS3), an applicationthat allows data to be analyzedfrom any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desiredoutput while using JAS3 to analyzethe results or output. Before an implementationof a neural network can take place, a firm understandingof what a neural network is and how it works is beneficial. -
Perspectives on Visual Search Technology What Is Visual Search? Retailers Are Looking for New Ways to Streamline Product Discovery and Visual Search Is Enabling Them
July 2019 ComCap’s perspectives on Visual Search technology What is Visual Search? Retailers are looking for new ways to streamline product discovery and Visual Search is enabling them ▪ At it’s core, Visual Search can answer key questions that are more easily resolved using visual prompts, unlike traditional text-based searching − From a consumer standpoint, visual search enables product discovery, connecting consumers directly to the products they desire ▪ Visual search is helping blend physical experiences and online convenience as retailers are competing to offer the best service for high-demanding customers through innovative technologies ▪ Consumers are now actively seeking multiple ways to visually capture products across channels, resulting in various applications including: − Image Recognition: enabling consumers to connect pictures with products, either through camera- or online- produced snapshots through AI-powered tools − QR / bar code scanning: allowing quick and direct access between consumers and specific products − Augmented Reality enabled experiences: producing actionable experiences by recognizing objects and layering information on top of a real-time view Neiman Marcus Samsung Google Introduces Amazon Introduces Snap. Introduces Bixby Google Lens Introduces Find. Shop. (2015) Vision (2017) (2017) StyleSnap (2019) Source: eMarketer, News articles 2 Visual Search’s role in retail Visual Search capabilities are increasingly becoming the industry standard for major ecommerce platforms Consumers place more importance on -
Microsoft ML for Apache Spark
Microsoft ML for Apache Spark Unifying Machine Learning Ecosystems at Massive Scales Mark Hamilton Microsoft, MIT [email protected] Overview Background Spark + SparkML MMLSpark Unifying ML Ecosystems LightGBM, CNTK, Vowpal Wabbit Vowpal Wabbit LightGBM CNTK Multilingual Bindings Microservice Orchestration Cognitive Services on Spark Cognitive Services Kubernetes Model Deployment with Spark Serving Use Cases The Snow Leopard Trust ● Code Driver ● Data Worker Worker Worker Age: Name: Age: Name: Age: Name: Int String Int String Int String A fault-tolerant distributed 15 Bob 1 Sam 25 Bob computing framework 25 Alice 2 Claire 25 Tom 33 Mary 53 Tina 66 Ted Map Reduce + SQL Data Source Whole program optimization + SQL, AWS Bucket, Local, CosmosDB, etc… query pushdown 120 110 Elastic 100 80 Scala, Python, R, Java, Julia 60 40 20 ML, Graph Processing, Streaming 0.9 0 Running Time(s) Hadoop Spark ML High level library for distributed machine learning More general than SciKit-Learn All models have a uniform interface Load Data Can compose models into Tokenizer Load Data complex pipelines Term Hashing Pipeline Can save, load, and transport Logistic Reg Evaluate models ● Estimator Evaluate ● Transformer Microsoft Machine Learning for Apache Spark v0.18 Microsoft’s Open Source Contributions to Apache Spark #UnifiedAnalytics #SparkAISummit 5 Distributed Fast Model Microservice Multilingual Binding Machine Learning Deployment Orchestration Generation www.aka.ms/spark Azure/mmlspark Unifying Machine Learning Ecosystems Goals -
European Journal of Computer Science and Information
European Journal of Computer Science and Information Technology Vol.9, No.2, pp.21-28, 2021 Print ISSN: 2054-0957 (Print), Online ISSN: 2054-0965 (Online) COMPARING THE TECHNOLOGIES USED FOR THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS Elda Xhumari PhD Candidate, University of Tirana, Faculty of Natural Sciences, Department of Informatics ABSTRACT: In the past few years, artificial neural networks (ANNs) have become a practice wherever it is necessary to solve problems of forecasting, classification, or control. ANNs are intuitively attractive because they are based on a primitive biological model of nervous systems. In the future, the development of such neurobiological models may lead to the creation of truly thinking computers. The areas of application of neural networks are very diverse - these are text and speech recognition, semantic search, expert systems, and decision support systems, prediction of stock prices, security systems, text analysis, etc. Based on the wide application of artificial neural networks, the application of numerous and diverse technologies can be inferred. In this research paper, a comparative approach towards the above-mentioned technologies will be undertaken in order to identify the optimal technology for each problem area in accordance with their respective advantages and disadvantages. KEYWORDS: artificial neural networks, applications, modules INTRODUCTION Artificial Neural Network (ANN) is a computational method that integrates the concepts of the human biological brain to computers for machine learning. The main purpose is to enable computers to adapt and learn the exact behaviour of human beings. As such, machine learning facilitates the continued processing of information that leads to the capacity to solve complex problems and equations that are beyond human ability (Goncalves et al., 2013).