Rohana Rajapakse is a senior developer at GOSS Interactive Ltd in Plymouth, UK. He received his PhD in Computer Science from the University of Plymouth in 2004. His research interests include digital information management, text processing, and neural networks. He has published several journal articles and conference papers on topics such as adaptive information retrieval, document categorization, and computational linguistics.
This document provides information on the "Intelligent Systems" module, including its code, level, credit points, location, coordinator, content, aims, learning outcomes, teaching methods, and assessment. The module introduces students to intelligent techniques like fuzzy logic, neural networks, and genetic algorithms through both theoretical and practical lessons. Students will learn to design and implement intelligent systems using MATLAB software. Assessment includes coursework assignments and a final written exam.
Phrase Structure Identification and Classification of Sentences using Deep Le...ijtsrd
?
Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi ""Phrase Structure Identification and Classification of Sentences using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
This document summarizes the ICOM project which researched computational intelligence, its principles, and applications. The project developed and implemented neural, symbolic, and hybrid systems including theory refinement systems, ANN compilers, genetic algorithms, and applications in various domains. Key developments included the CIL2P system which combines logic programming and neural networks, and rule extraction methods to explain neural network decisions. The combinatorial neural model was also investigated as a way to integrate neural and symbolic processing for classification tasks.
1. AI can be categorized as either strong general AI, capable of human-level intelligence across domains, or narrow AI, focused on specific problem sets.
2. For an AI system to be intelligent, it needs the abilities to receive and process information, remember, learn and abstract, model, plan, act, and evaluate progress.
3. Knowledge representation is crucial for AI, allowing knowledge to be structured in a way that facilitates efficient reasoning and inference. Common methods include semantic networks, description logics, and ontologies.
The tutorial has been presented at CAISE 2010. The tutorial discusses the state-of-the-art on research addresseing the quality of data at the conceptual level (conceptual schemas) and of Ontologies
A Tableau-based Federated Reasoning Algorithm for Modular OntologiesJie Bao
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This document describes a tableau-based algorithm for distributed reasoning over modular ontologies. It introduces description logics and modular ontologies modeled as packages in package-based description logics (P-DL). P-DL allows ontologies to be organized into modules or packages that can import terms from other packages. The algorithm uses a federation of local reasoners, each handling a package, to collaboratively construct a distributed tableau by sharing facts between local tableau constructions. This avoids materializing a single global tableau and allows reasoning to be performed even when global knowledge is not available.
This document provides a summary of the third edition of the textbook "C++ Plus Data Structures" by Nell Dale. The summary includes:
1) Key changes from the second to third edition include increased emphasis on object-oriented design, testing, and the inclusion of the abstract data type set.
2) The textbook covers abstract data types from three perspectives - specification, application, and implementation - across nine chapters focusing on common data structures and algorithms.
3) Each chapter presents an abstract data type, discusses its specification and applications, and provides C++ implementations and examples.
This document provides information about a Database Management Systems (DBMS) Laboratory course with mini project at Maharaja Institute of Technology Mysore. It includes the vision, mission, and objectives of the institution and computer science department. The document outlines the course objectives, outcomes, syllabus, and 5 sample database schemas to write SQL queries on. The course aims to teach foundational DBMS concepts, SQL programming skills, and developing database applications using front-end and back-end tools. Students will analyze real-world requirements and design database structures to solve problems through hands-on SQL queries and a mini project.
Dr. Gopal Dixit has over 7 years of experience in research and development of scientific software. He has strong technical and analytical skills in machine learning, data analysis, and visualization. He is capable of independent and efficient work with quick learning abilities. Dr. Dixit is seeking a data analyst position where he can apply his extensive experience in scientific research.
Lei Zheng has over 15 years of experience in areas such as machine learning, data mining, and software development. He currently works as a Senior Software Engineer at Yahoo, where he develops algorithms for spam filtering and detection of abusive behavior. Previously he held research positions at the University of Pittsburgh and JustSystems Evans Research, where he implemented algorithms and systems for information retrieval, natural language processing, and data mining.
Imran Khan is a PhD candidate at Shenzhen Institutes of Advanced Technology in China. His research focuses on ensemble clustering methods and algorithms for analyzing smart grid streaming data. He has a strong publication record, including papers in Neurocomputing and the International Journal of Data Science. Prior to his PhD studies, Khan worked as a lecturer and database architect. He is seeking opportunities to apply his skills and expertise in a prestigious organization.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Daniel Oblinger received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has over 20 years of experience in machine learning, data mining, and artificial intelligence research at IBM T.J. Watson Research Center. His research interests include programming by demonstration, statistical pattern recognition, and data mining of email, speech, and protein sequences. He has authored over a dozen patents and publications in major conferences and journals.
Daniel Oblinger received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has over 20 years of experience in machine learning, data mining, and artificial intelligence research at IBM T.J. Watson Research Center. His research interests include programming by demonstration, statistical pattern recognition, and data mining of email, speech, and protein sequences. He has authored over a dozen patents and publications in major conferences and journals.
This document provides a summary of Avinash Malik's education, employment history, projects, teaching experience, and publications. Some key details:
- Avinash Malik received his PhD in Electrical and Computer Engineering from the University of Auckland in 2010, with a thesis on a new system-level programming language called SystemJ.
- He is currently a Lecturer at the University of Auckland, where he has supervised several PhD and masters students. His projects include work on peer-to-peer lending portfolio optimization and heart modeling from ECG data.
- Previous positions include postdoctoral research roles at Trinity College Dublin, IBM Research Dublin, and INRIA Grenoble-Rh?ne
This document provides a summary of a candidate's skills and work experience for a position in analytics, data mining, and machine learning. The candidate has over 15 years of experience in data analysis, machine learning, artificial intelligence, and developing predictive models. They have extensive experience developing fraud detection models for credit cards and other domains. They also have a PhD in Computer Science and have published papers in conferences on topics like decision trees and feature selection.
Minita Jalan Shah is pursuing a Master of Science in Computer Science (Computational Biology) at Columbia University. She has relevant experience as a summer intern at the Itsik Pe'er Lab of Computational Genetics at Columbia, where she analyzed human genome data. She received her Bachelor of Science in Computer Engineering from Fr. Conceicao Rodrigues College of Engineering in Mumbai, India, where she developed several projects including a tool for identity-by-descent analysis and modeling of microRNA evolution.
The document discusses domain modeling for personalized learning. It defines a domain model as representing domain knowledge through concepts and their relationships. Domain models serve as the basis for individual student models and for indexing and classifying learning content. They can be used to model student knowledge and decide on appropriate next steps for learning. The document describes different types of domain models, including vector, network, conceptual, and procedural models. It also discusses using ontologies and different aspects in domain modeling and applying domain models to student modeling, content indexing, and personalized guidance.
AIAA Conference - Big Data Session_ Final - Jan 2016Manjula Ambur
?
The NASA Langley Research Center focuses on several technical areas including aerosciences, materials, modeling and simulation, and advanced IT. It aims to develop a "virtual research partner" using big data analytics, machine learning, and cognitive computing to gain insights from large datasets. Several pilot projects are exploring techniques like anomaly detection, time series analysis, and knowledge graphing to analyze materials testing, aeroelasticity, and cognitive state data. The goals are to automate tasks, discover new correlations, and develop virtual assistants to augment expert decision making.
Karen Hovsepian is seeking a position in computer science research and development, particularly in data mining applications related to finance and bioinformatics. She has a Ph.D. in computer science from New Mexico Tech and has developed several machine learning and data mining tools, including algorithms for classification, clustering, and volatility prediction. She has also taught various computer science courses as an instructor at New Mexico Tech.
Kadir A. Peker is an assistant professor in the Department of Computer Engineering at Melik?ah University in Turkey. His research interests include machine learning, computer vision, and deep learning, with a focus on convolutional neural networks for image matching and comparison. He has over 17 years of experience in academia and industry, teaching courses in programming, machine learning, and computer vision.
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
This document contains the resume of Rajendra Prasath, who holds a Ph.D in Mathematics from the University of Madras. He is currently a postdoctoral fellow at NTNU in Norway. His areas of research include textual case-based reasoning, machine learning, and complex networks. He has published papers in various international journals and conferences and has worked on projects related to information retrieval, text categorization, and distributed algorithms.
Sahil Grover is a final year undergraduate student studying Computer Science and Engineering at IIT Kanpur. He has a strong academic record and has received several awards and honors. His skills include proficiency in languages like C++, JavaScript, Python, and tools like Git. He has experience with projects involving machine learning, compilers, and operating systems. He also has extensive achievements in competitive programming competitions.
Sima Das is a PhD candidate at Missouri University of Science and Technology studying computer science. She has published several papers on analyzing large scale networks and predicting future links and contacts. Her research interests include information diffusion, temporal centrality metrics, and their effects in predicting mobility in dynamic networks. She has experience working on projects related to secure sensor cloud computing, green computing, wireless sensor networks, and mobile data management.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los h¨¢bitos de consumo causado por las nuevas tecnolog¨ªas. Describe c¨®mo YouTube aprovecha la participaci¨®n de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
This document provides a summary of the third edition of the textbook "C++ Plus Data Structures" by Nell Dale. The summary includes:
1) Key changes from the second to third edition include increased emphasis on object-oriented design, testing, and the inclusion of the abstract data type set.
2) The textbook covers abstract data types from three perspectives - specification, application, and implementation - across nine chapters focusing on common data structures and algorithms.
3) Each chapter presents an abstract data type, discusses its specification and applications, and provides C++ implementations and examples.
This document provides information about a Database Management Systems (DBMS) Laboratory course with mini project at Maharaja Institute of Technology Mysore. It includes the vision, mission, and objectives of the institution and computer science department. The document outlines the course objectives, outcomes, syllabus, and 5 sample database schemas to write SQL queries on. The course aims to teach foundational DBMS concepts, SQL programming skills, and developing database applications using front-end and back-end tools. Students will analyze real-world requirements and design database structures to solve problems through hands-on SQL queries and a mini project.
Dr. Gopal Dixit has over 7 years of experience in research and development of scientific software. He has strong technical and analytical skills in machine learning, data analysis, and visualization. He is capable of independent and efficient work with quick learning abilities. Dr. Dixit is seeking a data analyst position where he can apply his extensive experience in scientific research.
Lei Zheng has over 15 years of experience in areas such as machine learning, data mining, and software development. He currently works as a Senior Software Engineer at Yahoo, where he develops algorithms for spam filtering and detection of abusive behavior. Previously he held research positions at the University of Pittsburgh and JustSystems Evans Research, where he implemented algorithms and systems for information retrieval, natural language processing, and data mining.
Imran Khan is a PhD candidate at Shenzhen Institutes of Advanced Technology in China. His research focuses on ensemble clustering methods and algorithms for analyzing smart grid streaming data. He has a strong publication record, including papers in Neurocomputing and the International Journal of Data Science. Prior to his PhD studies, Khan worked as a lecturer and database architect. He is seeking opportunities to apply his skills and expertise in a prestigious organization.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Daniel Oblinger received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has over 20 years of experience in machine learning, data mining, and artificial intelligence research at IBM T.J. Watson Research Center. His research interests include programming by demonstration, statistical pattern recognition, and data mining of email, speech, and protein sequences. He has authored over a dozen patents and publications in major conferences and journals.
Daniel Oblinger received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has over 20 years of experience in machine learning, data mining, and artificial intelligence research at IBM T.J. Watson Research Center. His research interests include programming by demonstration, statistical pattern recognition, and data mining of email, speech, and protein sequences. He has authored over a dozen patents and publications in major conferences and journals.
This document provides a summary of Avinash Malik's education, employment history, projects, teaching experience, and publications. Some key details:
- Avinash Malik received his PhD in Electrical and Computer Engineering from the University of Auckland in 2010, with a thesis on a new system-level programming language called SystemJ.
- He is currently a Lecturer at the University of Auckland, where he has supervised several PhD and masters students. His projects include work on peer-to-peer lending portfolio optimization and heart modeling from ECG data.
- Previous positions include postdoctoral research roles at Trinity College Dublin, IBM Research Dublin, and INRIA Grenoble-Rh?ne
This document provides a summary of a candidate's skills and work experience for a position in analytics, data mining, and machine learning. The candidate has over 15 years of experience in data analysis, machine learning, artificial intelligence, and developing predictive models. They have extensive experience developing fraud detection models for credit cards and other domains. They also have a PhD in Computer Science and have published papers in conferences on topics like decision trees and feature selection.
Minita Jalan Shah is pursuing a Master of Science in Computer Science (Computational Biology) at Columbia University. She has relevant experience as a summer intern at the Itsik Pe'er Lab of Computational Genetics at Columbia, where she analyzed human genome data. She received her Bachelor of Science in Computer Engineering from Fr. Conceicao Rodrigues College of Engineering in Mumbai, India, where she developed several projects including a tool for identity-by-descent analysis and modeling of microRNA evolution.
The document discusses domain modeling for personalized learning. It defines a domain model as representing domain knowledge through concepts and their relationships. Domain models serve as the basis for individual student models and for indexing and classifying learning content. They can be used to model student knowledge and decide on appropriate next steps for learning. The document describes different types of domain models, including vector, network, conceptual, and procedural models. It also discusses using ontologies and different aspects in domain modeling and applying domain models to student modeling, content indexing, and personalized guidance.
AIAA Conference - Big Data Session_ Final - Jan 2016Manjula Ambur
?
The NASA Langley Research Center focuses on several technical areas including aerosciences, materials, modeling and simulation, and advanced IT. It aims to develop a "virtual research partner" using big data analytics, machine learning, and cognitive computing to gain insights from large datasets. Several pilot projects are exploring techniques like anomaly detection, time series analysis, and knowledge graphing to analyze materials testing, aeroelasticity, and cognitive state data. The goals are to automate tasks, discover new correlations, and develop virtual assistants to augment expert decision making.
Karen Hovsepian is seeking a position in computer science research and development, particularly in data mining applications related to finance and bioinformatics. She has a Ph.D. in computer science from New Mexico Tech and has developed several machine learning and data mining tools, including algorithms for classification, clustering, and volatility prediction. She has also taught various computer science courses as an instructor at New Mexico Tech.
Kadir A. Peker is an assistant professor in the Department of Computer Engineering at Melik?ah University in Turkey. His research interests include machine learning, computer vision, and deep learning, with a focus on convolutional neural networks for image matching and comparison. He has over 17 years of experience in academia and industry, teaching courses in programming, machine learning, and computer vision.
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
This document contains the resume of Rajendra Prasath, who holds a Ph.D in Mathematics from the University of Madras. He is currently a postdoctoral fellow at NTNU in Norway. His areas of research include textual case-based reasoning, machine learning, and complex networks. He has published papers in various international journals and conferences and has worked on projects related to information retrieval, text categorization, and distributed algorithms.
Sahil Grover is a final year undergraduate student studying Computer Science and Engineering at IIT Kanpur. He has a strong academic record and has received several awards and honors. His skills include proficiency in languages like C++, JavaScript, Python, and tools like Git. He has experience with projects involving machine learning, compilers, and operating systems. He also has extensive achievements in competitive programming competitions.
Sima Das is a PhD candidate at Missouri University of Science and Technology studying computer science. She has published several papers on analyzing large scale networks and predicting future links and contacts. Her research interests include information diffusion, temporal centrality metrics, and their effects in predicting mobility in dynamic networks. She has experience working on projects related to secure sensor cloud computing, green computing, wireless sensor networks, and mobile data management.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los h¨¢bitos de consumo causado por las nuevas tecnolog¨ªas. Describe c¨®mo YouTube aprovecha la participaci¨®n de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
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This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
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The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - ºÝºÝߣ 1butest
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The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
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Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
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This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. Rohana Rajapakse, Ph.D.
267 North Road West, Plymouth UK, PL1 5DH
Telephone (Home): +44-1752-603095
E-mail: rrajapakse@plymouth.ac.uk
EDUCATION
PhD University of Plymouth, UK 2004 Computer Science
MSc University of Plymouth, UK 1998 Computational Intelligence
MSc University of Colombo, Sri Lanka 1995 Computer Science
BSc University of Colombo, Sri Lanka 1986 Mathematics
CORE SKILLS
? Information processing, Management and Computational Linguistics:
? Text Processing/Retrieval techniques: text extraction, representation, retrieval
and query reformulation.
? Document categorisation for Knowledge Management (Meta-tagging).
? Adaptive information retrieval using Neural Networks for concept
representation and concept matching.
? Programming, Tools and Environments:
? Solid, long-term kills in Java.
? MatLab and CortexPro neural network simulator.
? Eclipse IDE, JUnit unit testing, Subversion (SVN), Tortoise for SVN, Maven
? Computational Intelligence:
? Neural nets, reinforcement learning, genetic algorithms and fuzzy set theory.
Experienced in using a number of different neural network architectures
including MLP, RBF, Elman nets and Bidirectional Associative Memories
(BAMs).
? Good experience of hands-on implementation and experimentation with such
techniques for important real-world problems.
? Interested in biologically inspired machine vision (object identification,
spatial relationships etc.).
? Excellent engineering and mathematical problem solving abilities.
? Substantial knowledgeable and experienced of conducting research (independently
and team project-based), reporting and publishing results.
SPECIALISED TRAINING
Mar/2007 (one week): J2EE¨C JB International Ltd., London.
Nov/2006 (one week): Business Leadership.
Jan/2006 (4 days) : Advanced Java programming ¨C JB International Ltd., London.
2. Nov/2005 (one week) : Peoples Skills/Management ¨C Insight People Development Ltd, UK
Oct/2005 (one week) : Managing for Results/ Project Management - Insight People
Development Ltd, UK
Feb/2002-April/2002 : Teaching techniques course ¨C University of Plymouth, UK
Jan/1998-Mar/1998 : Case Tools Based Object Oriented Systems Design- Tokyo, Japan
Nov/1995-Mar/1996 : Personal Computer Network Systems Designer - Okinawa, Japan
PRESENT EMPLOYMENT
Aug/2005 ¨C To date Senior Developer,
GOSS Interactive Ltd,
IITC, Tamar Science Park,
1 Davy Road, Derriford, Plymouth, UK, PL6 8BX
The aim of the project is to develop automatic meta-tagging techniques to extract
appropriate subject topics from taxonomies. Development of techniques and models
using open source software including ¡°Kea¡± keyphrase extraction tools and Lucene
search engine. Also implemented novel learning mechanisms for user preference
adaptation. The techniques developed are integrated into a content management
software. One publication has been made and three are in preperations.
PREVIOUS EMPLOYMENT
Dec/2003-Aug/2005 Postdoctoral Research Fellow,
School of Computing Communications and Electronics,
University of Plymouth, UK, PL4 8AA
Responsible for developing a connectionist model for grounding spatial prepositions and
quantifiers in English language. Created a neural network model that can reproduce the
acceptability ratings given by human subjects for a set of visual stimuli. The model
includes a vision processing module with Gaussian receptive fields to extract salient
features of input stimuli to create vector representations. These are used to train the
neural network module(s) to produce the acceptability ratings.
Mar/1991-Nov/1999 Systems Analyst/Programmer,
Department of Statistics and Computer Science,
University of Colombo, Sri Lanka.
A number of responsibilities involved in this employment. In addition to systems analysis
and development, other responsibilities included teaching and tutoring undergraduate and
postgraduate computer science modules, administration of computer systems, networks
and labs, and consultancy work regarding the design and development of departmental
software. Furthermore, course material were developed for few course modules and was
the line manager of junior demonstrators and instructors.
3. TEACHING
Undergraduate Level
? Neural Computing ¡¡¡¡¡¡¡... ¡.Undergraduate- Level3 and MSc
? C Programming¡¡¡¡¡¡¡¡¡. ¡.Undergraduate teaching ¨C Level3
? Parallel Processing¡¡¡¡¡¡¡¡ ¡.Undergraduate teaching ¨C Level3
? Fuzzy Logic & Fuzzy Systems¡¡¡. ¡.Undergraduate Teaching ¨C Level3
? Computer literacy courses on¡¡¡... ¡.Undergraduate teaching and lab
Application Packages demonstrations for non computing degrees
? Computing for Lawyers...................... ¡.Undergraduate teaching ¨C level1
? Information Retrieval........................... ¡.Undergraduate seminars ¨C Level2
? Introduction to Information.................. ¡.Undergraduate teaching and lab
Technology I (Access, Excel & Word) demonstration ¨C Level1
COURSE DEVELOPMENT
? Designed and developed ? a course on ¡°Neural Networks¡± for undergraduates and ? a
course for postgraduates.
? Designed and developed ? a course on Fuzzy Systems for undergraduates.
? Designed and developed ? a course on C/C++ programming for undergraduates.
? Designed and developed a number of computer literacy courses on Microsoft office
suit.
ACADEMIC HONORS AND AWARDS
? Distinction pass- MSc in Computational Intelligence at Univ. of Plymouth, UK.
? Fist Class pass ¨C BSc in Mathematics at Univ. of Colombo, Sri Lanka.
? World Bank Scholarship for the MSc at Univ. of Plymouth, UK.
? Studentship/Bursary for the PhD from the Univ. of Plymouth, UK.
? Excellent Achievement Award - OO Designer course, CICC, Japan.
PUBLICATIONS
Journal Publications / Book Chapters:
? Rajapakse R.K., Johnson, C. and Gant, N. (under review). ¡°Feature learning for adaptive
document categorisation¡±. International Journal of Human-Computer Studies.
? Rajapakse R.K., Mushens, B. and Gant, N. (under review). ¡°Dynamic weight step
computation for user-oriented adaptive concept learning¡±. Journal of Information
Processing and Management.
? Rajapakse R.K., Mushens, B. and Phippen, A. (under review). ¡°Automated subject
categorisation with controlled vocabularies¡±, Journal of Information Processing &
Management.
? Rajapakse R.K., Mushens, B. and Johnson, C. (2007), ¡°The use of keyphrases for
selecting metadata from taxonomies¡±, Book chapter in ¡°Creating Collaborative
Advantage Through Knowledge and Innovation¡±, Series on Innovation and Knowledge
Management, Vol. 5, edited by Suliman Hawamdeh (Oklahoma University, USA),
4. World Scientific Publishing Company Ltd. Singapore.
? Rajapakse R.K. and Denham M. (2006), ¡°Text Retrieval with more Realistic Concept
Matching and Reinforcement Learning", Journal of Information Processing &
Management, Vol.42. pp1260-1275.
? Rajapakse R.K, Denham M. (2005), ¡°Fast Access to Concepts in Concept Lattices via
Bidirectional Associative Memories¡±, Journal of Neural Computation, Vol.17(10).
? Coventry, K.R., Cangelosi, A., Rajapakse, R., Bacon, A., Newstead, S. Joyce, D., and
Richards, L.V.(2005), ¡°Spatial prepositions and vague quantifiers: Implementing the
functional geometric framework. In C. Freksa, B. Nebel, M. Knuff & B. Krieg-Bruckner
(Eds.), Spatial Cognition, Volume IV. Reason Action and Interaction, pp 98-110. Lecture
notes in Artificial Intelligence, Springer Verlag Book chapter).
Conference / Workshop Proceedings:
? Rajapakse, R.K. (2007) ¡°Structuring the Unstructured: Metadata for Knowledge
Management¡±. In proceedings of the South West Regional Seminar, 14th November
2007, University of Plymouth.
? Rajapakse, R.K., Cangelosi, A., Coventry, K.R., Newstead, S. and Bacon, A., (2005)
"Connectionist Modeling of Linguistic Quantifiers", International Conference on
Artificial Neural Networks (ICANN-2005), September 11-15, 2005, Warsaw, Poland.
? Coventry K.R., Cangelosi A., Joyce, D., Rajapakse, R., Bacon A., and Richards, L. V. (in
preparation) ¡°Spatial Language and perceptual symbols: Implementing the functional
geometric framework. For submission to Psychological Review or Cognitive
Psychology.
? Coventry K.R., Cangelosi A., Bacon A., Newstead, S. and Rajapakse, R. K. (2005)
¡°Grounding number in perception: Vague Quantifiers and Visual Attention", Cognitive
Psychology.
? Coventry K.R., Cangelosi A., Newstead S.N., Bacon A., Rajapakse R., (2005)
¡°Grounding natural language quantifiers in visual attention¡±. Twenty-seventh annual
conference of Cognitive Science Society, Stresa, Italy.
? Rajapakse, R.K., Cangelosi, A., Coventry, K.R., Newstead, S. and Bacon, A.,(2005)
"Grounding Linguistic Quantifiers in Perception: Experiments on Numerosity
Judgments", Proceedings of the 2nd Language and Technology Conference: Human
Language Technologies as a challenge for Computer Science and Linguistics (L&T'05),
April 21-23, 2005, Poznan, Poland. Publisher: Wydawnictwo Poznanskie Sp. z o.o.
ISBN 83-7177-341-2.
? Cangelosi, A., Coventry, K.R., Rajapakse, R., Bacon, A., and Newstead, S.N.(2005),
Grounding language in perception: A connectionist model of spatial terms and vague
quantifiers. In A. Cangelosi, G. Bugmann and R. Borisyuk, editors, Modelling Language,
Cognition and Action: Proceedings of the 9th Neural Computation and Psychology
Workshop. Singapore: World Scientific, (Book chapter).
? Cangelosi A., Coventry K.R., Rajapakse R., Bacon A. & Newstead S.N. (2004),
Grounding language in perception: A connectionist model of spatial terms and vague
quantifiers. In Book of Abstracts of the 9th Neural Computation and Psychology
Workshop (NCPW9), Plymouth, 2004, p. 16
? Rajapakse, R.K. and Denham, M. (2003), ¡°A Reinforcement Learning Strategy for
(formal) Concept and Keyword Weight Learning for Adaptive Information Retrieval¡± ,
Proceedings of the 7th World Multiconference on Systemics Cybernetics and Informatics
(SCI¡¯2003), Orlando, Florida USA, July 2003. Also appeared in the workshop
5. proceedings of the ¡°User Modeling, Information Retrieval and Machine Learning¡±
workshop of the 9th International Conference on User Modeling (UM¡¯2003).
? Rajapakse, R.K. and Denham, M. (2002), ¡°Information Retrieval Model Using Concept
Lattices for Content Representation¡±, FCA KDD workshop of the 15th European
Conference on Artificial Intelligence (ECAI¡¯02), July 21-26 2002, Lyon, France.
? Rajapakse, R.K. and Denham, M. (2002),¡°A Concept based Adaptive Information
Retrieval Model using FCA-BAM combination for concept representation¡±, Proceedings
of the 24th BCS-IRSG European Colloquium on IR Research (ECIR¡¯02), March 25-27,
2002, Glasgow, UK
? Rajapakse, R.K., Seneviratne, K. and Weerasinghe, R. (1996),"A Neural Network Based
Character Recognition System for ¡®Sinhala¡¯ Script", Proceedings of the South East Asian
Regional Computer Confederation, Conference and Cyberexhibition (SEARCC'96),
Bangkok , Thailand , July 4 - 7th 1996
RESEARCH INTERESTS
Digital information management, meta-tagging for knowledge management
Text processing, Intelligent information retrieval, Knowledge representation
Neural Networks and Adaptive behavioursystems.
Machine Learning and pattern recognition.
Computer vision based on human vision system.
REFERENCES
o Prof. Mike Denham
University of Plymouth,
Plymouth PL4 8AA
Email : mdenham@plymouth.ac.uk
o Prof. Angelo Cangelosi
Professor in Artificial Intelligence and Cognition
School of Computing, Communications and Electronics,
University of Plymouth,
Plymouth PL4 8AA
Email : acangelosi@plymouth.ac.uk
Tel : +(44)-(0)1752-232559 Fax: + (44)-(0)1752-232540
o Mr. Brian Mushens,
Program Director (Computing),
School of Computing Communications and Electronics,
University of Plymouth,
Plymouth PL4 8AA
Email : bmushens@plymouth.ac.uk
Tel : +(44)-(0)1752-232544 Fax: +(44)-(0)1752-232540