The document describes a project report submitted by three students - Prasanth, Surya, and Vembarasu - for their bachelor's degree in computer science and engineering. It discusses developing a system called "Relevant Multimedia Question Answering" that can enrich textual answers found in community question answering forums by adding relevant images and videos. The system aims to determine what type of media to add, generate queries to search for multimedia data, and select and present appropriate images and videos to enhance the original textual answers.
Multimedia Answer Generation for Community Question AnsweringSWAMI06
Community question answering (cQA) services have
gained popularity over the past years. It not only allows community
members to post and answer questions but also enables general
users to seek information froma comprehensive set of well-answered
questions. However, existing cQA forums usually provide
only textual answers, which are not informative enough for many
questions. In this paper, we propose a scheme that is able to enrich
textual answers in cQA with appropriate media data. Our
scheme consists of three components: answer medium selection,
query generation for multimedia search, and multimedia data selection
and presentation. This approach automatically determines
which type of media information should be added for a textual answer.
It then automatically collects data from the web to enrich the
answer. By processing a large set of QA pairs and adding them to
a pool, our approach can enable a novel multimedia question answering
(MMQA) approach as users can find multimedia answers
by matching their questions with those in the pool. Different from a
lot ofMMQAresearch efforts that attempt to directly answer questions
with image and video data, our approach is built based on
community-contributed textual answers and thus it is able to deal
with more complex questions.We have conducted extensive experiments
on a multi-source QA dataset. The results demonstrate the
effectiveness of our approach.
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
IRJET-Semi-Supervised Collaborative Image Retrieval using Relevance FeedbackIRJET Journal
This document describes a semi-supervised collaborative image retrieval system using relevance feedback. The system aims to improve the performance of content-based image retrieval (CBIR) systems by reducing the number of images a user must label during relevance feedback. It uses a semi-supervised approach where the user only needs to label a few of the most informative images. These labeled images are used to train a support vector machine (SVM) classifier. The images in the database are then resorted based on a new similarity metric determined by the classifier. The system provides iteratively improved retrieval results until the user is satisfied, thereby bridging the semantic gap between low-level visual features and high-level semantics.
This document presents a system for detecting semantically similar questions in online forums like Quora to reduce duplicate content. It proposes using natural language processing techniques like tagging questions with keywords, vectorizing text with Google News vectors, and calculating similarity with Word Mover's Distance. The system cleans and preprocesses questions before generating tags and calculating similarity between questions to identify duplicates. An evaluation of the system achieved accurate detection of matching and non-matching question pairs.
IRJET-Image Question Answering: A ReviewIRJET Journal
This document provides a review of image question answering, which involves understanding visual elements in an image and common-sense knowledge to provide responses to open-ended questions about the image. It discusses approaches that map images and questions to a common feature space, along with datasets used to train and evaluate these systems. Several existing datasets for image question answering are described and compared, including DAQUAR, COCO-QA, VQA, and FM-IQA. Algorithms for image question answering extract features from the image and question and combine them to generate an answer, and baseline models are used as starting points for evaluation.
Répondre à la question automatique avec le webAhmed Hammami
This document summarizes an automatic question answering system that goes beyond answering simple factual questions. The system is trained on a corpus of 1 million question/answer pairs collected from frequently asked question pages on the web. It uses statistical models like a question chunker, answer/question translation model, and answer language model. The evaluation shows the system achieves reasonable performance on a variety of complex, non-factual questions by leveraging large web collections to find answers rather than assuming answers are short facts.
The document presents a model to predict question quality in community question answering sites. It aims to predict user satisfaction and question quality in both the online and offline scenarios. In the online scenario, it uses features from question text and the asker's profile, while in the offline scenario it adds features from community responses. Experimental results show that predicting satisfaction achieves 70% accuracy using logistic regression with additional text features. Community interaction features are more predictive than question content features alone. The model performs better at predicting unsatisfied questions.
Multimedia Answer Generation for Community Question AnsweringSWAMI06
Community question answering (cQA) services have
gained popularity over the past years. It not only allows community
members to post and answer questions but also enables general
users to seek information froma comprehensive set of well-answered
questions. However, existing cQA forums usually provide
only textual answers, which are not informative enough for many
questions. In this paper, we propose a scheme that is able to enrich
textual answers in cQA with appropriate media data. Our
scheme consists of three components: answer medium selection,
query generation for multimedia search, and multimedia data selection
and presentation. This approach automatically determines
which type of media information should be added for a textual answer.
It then automatically collects data from the web to enrich the
answer. By processing a large set of QA pairs and adding them to
a pool, our approach can enable a novel multimedia question answering
(MMQA) approach as users can find multimedia answers
by matching their questions with those in the pool. Different from a
lot ofMMQAresearch efforts that attempt to directly answer questions
with image and video data, our approach is built based on
community-contributed textual answers and thus it is able to deal
with more complex questions.We have conducted extensive experiments
on a multi-source QA dataset. The results demonstrate the
effectiveness of our approach.
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
IRJET-Semi-Supervised Collaborative Image Retrieval using Relevance FeedbackIRJET Journal
This document describes a semi-supervised collaborative image retrieval system using relevance feedback. The system aims to improve the performance of content-based image retrieval (CBIR) systems by reducing the number of images a user must label during relevance feedback. It uses a semi-supervised approach where the user only needs to label a few of the most informative images. These labeled images are used to train a support vector machine (SVM) classifier. The images in the database are then resorted based on a new similarity metric determined by the classifier. The system provides iteratively improved retrieval results until the user is satisfied, thereby bridging the semantic gap between low-level visual features and high-level semantics.
This document presents a system for detecting semantically similar questions in online forums like Quora to reduce duplicate content. It proposes using natural language processing techniques like tagging questions with keywords, vectorizing text with Google News vectors, and calculating similarity with Word Mover's Distance. The system cleans and preprocesses questions before generating tags and calculating similarity between questions to identify duplicates. An evaluation of the system achieved accurate detection of matching and non-matching question pairs.
IRJET-Image Question Answering: A ReviewIRJET Journal
This document provides a review of image question answering, which involves understanding visual elements in an image and common-sense knowledge to provide responses to open-ended questions about the image. It discusses approaches that map images and questions to a common feature space, along with datasets used to train and evaluate these systems. Several existing datasets for image question answering are described and compared, including DAQUAR, COCO-QA, VQA, and FM-IQA. Algorithms for image question answering extract features from the image and question and combine them to generate an answer, and baseline models are used as starting points for evaluation.
Répondre à la question automatique avec le webAhmed Hammami
This document summarizes an automatic question answering system that goes beyond answering simple factual questions. The system is trained on a corpus of 1 million question/answer pairs collected from frequently asked question pages on the web. It uses statistical models like a question chunker, answer/question translation model, and answer language model. The evaluation shows the system achieves reasonable performance on a variety of complex, non-factual questions by leveraging large web collections to find answers rather than assuming answers are short facts.
The document presents a model to predict question quality in community question answering sites. It aims to predict user satisfaction and question quality in both the online and offline scenarios. In the online scenario, it uses features from question text and the asker's profile, while in the offline scenario it adds features from community responses. Experimental results show that predicting satisfaction achieves 70% accuracy using logistic regression with additional text features. Community interaction features are more predictive than question content features alone. The model performs better at predicting unsatisfied questions.
Dental TutorBot: Exploitation of Dental Textbooks for Automated LearningSergey Sosnovsky
The document describes a proposed dental tutor chatbot system that would be trained on dental textbooks to provide automated learning for medical students. It would ask students questions to assess their knowledge and provide hints to help them learn. The proposed system would use natural language processing techniques like question answering, topic modeling, and hint generation based on information extracted from textbooks. It provides details on the methodology, including preprocessing text, generating questions and answers, extracting topics, and integrating the chatbot with platforms like WhatsApp. The goal is to leverage virtual education to help address gaps from a lack of in-person instruction during the pandemic.
VIDEO OBJECTS DESCRIPTION IN HINDI TEXT LANGUAGE ijmpict
Video activity recognition has grown to be a dynamic location of analysis in latest years. A widespread
information-driven approach is denoted in this paper that produces descriptions of video content into
textual content description inside the Hindi language. This method combines the final results of modern
item with "real-international" records to pick the in all subject-verb-object triplet for depicting a video. The
usage of this triplet desire technique, a video is tagged via the trainer, mainly, Subject, Verb, and object
(SVO) and then this data is mined to improve the result of checking out video clarification by using pastime
as well as item identity. Contrasting preceding approaches, this method can annotate arbitrary videos
deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
work affords initial and primary text description within the Hindi language that is producing easy words
and sentence formation. But the fundamental challenging attempt on this work is to extract grammatically
accurate and expressive text records in Hindi textual content regarding video content.
IRJET- Semantic Analysis of Online Customer QueriesIRJET Journal
1) The document describes a study on using machine learning algorithms like SVM and Naive Bayes for semantic analysis of customer queries received by chatbots.
2) It analyzes the performance of different algorithms on accuracy and the effect of increasing training samples and incorporating context from previous queries.
3) The results show that SVM and Naive Bayes perform better than other algorithms, and accuracy improves with more training samples and by maintaining context from previous queries.
QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGEijaia
This document discusses a proposed question answering system for the Marathi language that uses ontology as a knowledge base. The system aims to provide accurate answers to user questions in Marathi by analyzing queries semantically using ontologies. Ontologies are developed with help from domain experts and represent domain knowledge through semantic relations. The system first analyzes user questions syntactically and semantically. It then extracts candidate answers from the ontology and generates a precise answer in Marathi language to satisfy the original user query. The use of ontology for semantic analysis is meant to enhance the accuracy of answers provided by the question answering system.
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
This document provides an overview of machine learning with graphs. It discusses graph neural networks and deep learning in graphs. It covers representing graphs using adjacency matrices and lists. It also discusses node and graph level features, as well as node embeddings using random walks. Finally, it summarizes several graph neural network models like GCN and GraphSAGE and their applications to citation networks, social networks, and knowledge graphs.
Generating domain specific sentiment lexicons using the Web Directory acijjournal
In this paper we aim at proposing a method to automatically build a sentiment lexicon which is domain based. There has been a demand for the construction of generated and labeled sentiment lexicon. For data on the social web (E.g., tweets), methods which make use of the synonymy relation don't work well, as we completely ignore the significance of terms belonging to specific domains. Here we propose to
generate a sentiment lexicon for any domain specified, using a twofold method. First we build sentiment scores using the micro-blogging data, and then we use these scores on the ontological structure provided by Open Directory Project [1], to build a custom sentiment lexicon for analyzing domain specific microblogging data.
AI Chatbot Service Framework based on Backpropagation Network for Predicting ...資彥 解
We provide the framework to design AI Chatbot, It's use the Node.js Program Language and Facebook API, Based on Neural Network Algorithm, and we deploy this system on cloud platform as a web service.
Demo video: https://youtu.be/_3xyxJ-ACxM
Facebook page:https://www.facebook.com/MrWang-378725769139917/
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
IRJET - Artificial Conversation Entity for an Educational InstituteIRJET Journal
1) The document describes the design and implementation of an artificial conversation entity or chatbot for an educational institute to address student, staff, and public queries.
2) The chatbot uses natural language processing techniques like tokenization and stemming to process user inputs and matches keywords to responses stored in a database. It also uses a deep neural network for improved response selection.
3) The chatbot is intended to provide information on topics like admissions, fees, scholarships, library facilities, hostels, canteens, sports events, placements to help students, staff, and the public without needing to visit the institute physically. This reduces workload and makes information easily accessible.
IRJET - Chat-Bot for College Information System using AIIRJET Journal
This document describes a proposed chatbot for a college information system using artificial intelligence. The chatbot would be developed using natural language processing and artificial intelligence algorithms to analyze user queries about the college and provide appropriate responses. It would allow students to get information about college admissions, programs, activities and more without having to visit the college in person. The proposed system would work as a web application that uses techniques like stemming, lemmatization and sentiment analysis to understand questions and return relevant answers using a graphical interface similar to a human conversation. The goal is for students to easily get updated on college information and activities through an online chatbot system.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Convolutional recurrent neural network with template based representation for...IJECEIAES
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
This resume summarizes Kelvin Lo Yir Siang's qualifications. He has over 5 years of experience in software development and consulting roles. He is skilled in languages like C#, C++, and SQL. He holds a Master's degree in Computer Science. His goal is to obtain a software consultant position with a salary of SGD 5,000.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
QUrdPro: Query processing system for Urdu LanguageIJERA Editor
This document describes QUrdPro, a query processing system for the Urdu language. It proposes an ontology-based architecture that uses natural language processing to analyze user queries in Urdu, formulate queries based on the domain ontology, search documents to extract relevant answers, and return results to the user. The system aims to improve information retrieval for the Urdu language by leveraging ontologies and avoiding users having to sift through large amounts of unstructured text. It discusses related work on question answering systems and outlines the proposed architecture and four-phase process model of QUrdPro.
This document describes a cyberbullying detection model that uses machine learning techniques to overcome limitations of existing methods. It analyzes a Twitter dataset containing annotated tweets using natural language processing and classifiers like SVM, random forest, and KNN. The models achieved up to 95% accuracy in detecting cyberbullying posts. The authors propose expanding the model to use unsupervised learning, integrate with social media APIs to detect bullying in real-time, and develop image recognition to identify bullying across multiple media platforms.
Multimedia Technology
n Overview
q Introduction
q Chapter 1: Background of compression techniques
q Chapter 2: Multimedia technologies
n JPEG
n MPEG-1/MPEG -2 Audio & Video
n MPEG-4
n MPEG-7 (brief introduction)
n HDTV (brief introduction)
n H261/H263 (brief introduction)
n Model base coding (MBC) (brief introduction)
q Chapter 3: Some real-world systems
n CATV systems
n DVB systems
q Chapter 4: Multimedia Network
The document introduces multimedia and its uses. It defines multimedia as using more than one media element, such as text, graphics, sound, animation and video. Most multimedia is digitized and interactive, allowing users some control over the content. It is used in business, education, entertainment and on the internet. Careers in multimedia include positions in management, production, art, content and support.
This document introduces multimedia and its key elements. It defines multimedia as a combination of text, graphics, sound, animation and video delivered interactively. The 5 main elements are described as text, audio, graphics, video and animation. It also discusses linear vs non-linear multimedia, authoring tools, importance and applications of multimedia, and different types of multimedia products such as briefing, reference, database, education/training, kiosk and entertainment products.
Dental TutorBot: Exploitation of Dental Textbooks for Automated LearningSergey Sosnovsky
The document describes a proposed dental tutor chatbot system that would be trained on dental textbooks to provide automated learning for medical students. It would ask students questions to assess their knowledge and provide hints to help them learn. The proposed system would use natural language processing techniques like question answering, topic modeling, and hint generation based on information extracted from textbooks. It provides details on the methodology, including preprocessing text, generating questions and answers, extracting topics, and integrating the chatbot with platforms like WhatsApp. The goal is to leverage virtual education to help address gaps from a lack of in-person instruction during the pandemic.
VIDEO OBJECTS DESCRIPTION IN HINDI TEXT LANGUAGE ijmpict
Video activity recognition has grown to be a dynamic location of analysis in latest years. A widespread
information-driven approach is denoted in this paper that produces descriptions of video content into
textual content description inside the Hindi language. This method combines the final results of modern
item with "real-international" records to pick the in all subject-verb-object triplet for depicting a video. The
usage of this triplet desire technique, a video is tagged via the trainer, mainly, Subject, Verb, and object
(SVO) and then this data is mined to improve the result of checking out video clarification by using pastime
as well as item identity. Contrasting preceding approaches, this method can annotate arbitrary videos
deprived of wanting the large series and annotation of a similar schooling video corpus. The proposed
work affords initial and primary text description within the Hindi language that is producing easy words
and sentence formation. But the fundamental challenging attempt on this work is to extract grammatically
accurate and expressive text records in Hindi textual content regarding video content.
IRJET- Semantic Analysis of Online Customer QueriesIRJET Journal
1) The document describes a study on using machine learning algorithms like SVM and Naive Bayes for semantic analysis of customer queries received by chatbots.
2) It analyzes the performance of different algorithms on accuracy and the effect of increasing training samples and incorporating context from previous queries.
3) The results show that SVM and Naive Bayes perform better than other algorithms, and accuracy improves with more training samples and by maintaining context from previous queries.
QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGEijaia
This document discusses a proposed question answering system for the Marathi language that uses ontology as a knowledge base. The system aims to provide accurate answers to user questions in Marathi by analyzing queries semantically using ontologies. Ontologies are developed with help from domain experts and represent domain knowledge through semantic relations. The system first analyzes user questions syntactically and semantically. It then extracts candidate answers from the ontology and generates a precise answer in Marathi language to satisfy the original user query. The use of ontology for semantic analysis is meant to enhance the accuracy of answers provided by the question answering system.
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
This document provides an overview of machine learning with graphs. It discusses graph neural networks and deep learning in graphs. It covers representing graphs using adjacency matrices and lists. It also discusses node and graph level features, as well as node embeddings using random walks. Finally, it summarizes several graph neural network models like GCN and GraphSAGE and their applications to citation networks, social networks, and knowledge graphs.
Generating domain specific sentiment lexicons using the Web Directory acijjournal
In this paper we aim at proposing a method to automatically build a sentiment lexicon which is domain based. There has been a demand for the construction of generated and labeled sentiment lexicon. For data on the social web (E.g., tweets), methods which make use of the synonymy relation don't work well, as we completely ignore the significance of terms belonging to specific domains. Here we propose to
generate a sentiment lexicon for any domain specified, using a twofold method. First we build sentiment scores using the micro-blogging data, and then we use these scores on the ontological structure provided by Open Directory Project [1], to build a custom sentiment lexicon for analyzing domain specific microblogging data.
AI Chatbot Service Framework based on Backpropagation Network for Predicting ...資彥 解
We provide the framework to design AI Chatbot, It's use the Node.js Program Language and Facebook API, Based on Neural Network Algorithm, and we deploy this system on cloud platform as a web service.
Demo video: https://youtu.be/_3xyxJ-ACxM
Facebook page:https://www.facebook.com/MrWang-378725769139917/
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
IRJET - Artificial Conversation Entity for an Educational InstituteIRJET Journal
1) The document describes the design and implementation of an artificial conversation entity or chatbot for an educational institute to address student, staff, and public queries.
2) The chatbot uses natural language processing techniques like tokenization and stemming to process user inputs and matches keywords to responses stored in a database. It also uses a deep neural network for improved response selection.
3) The chatbot is intended to provide information on topics like admissions, fees, scholarships, library facilities, hostels, canteens, sports events, placements to help students, staff, and the public without needing to visit the institute physically. This reduces workload and makes information easily accessible.
IRJET - Chat-Bot for College Information System using AIIRJET Journal
This document describes a proposed chatbot for a college information system using artificial intelligence. The chatbot would be developed using natural language processing and artificial intelligence algorithms to analyze user queries about the college and provide appropriate responses. It would allow students to get information about college admissions, programs, activities and more without having to visit the college in person. The proposed system would work as a web application that uses techniques like stemming, lemmatization and sentiment analysis to understand questions and return relevant answers using a graphical interface similar to a human conversation. The goal is for students to easily get updated on college information and activities through an online chatbot system.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
Convolutional recurrent neural network with template based representation for...IJECEIAES
Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.
This resume summarizes Kelvin Lo Yir Siang's qualifications. He has over 5 years of experience in software development and consulting roles. He is skilled in languages like C#, C++, and SQL. He holds a Master's degree in Computer Science. His goal is to obtain a software consultant position with a salary of SGD 5,000.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
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This document describes a cyberbullying detection model that uses machine learning techniques to overcome limitations of existing methods. It analyzes a Twitter dataset containing annotated tweets using natural language processing and classifiers like SVM, random forest, and KNN. The models achieved up to 95% accuracy in detecting cyberbullying posts. The authors propose expanding the model to use unsupervised learning, integrate with social media APIs to detect bullying in real-time, and develop image recognition to identify bullying across multiple media platforms.
Multimedia Technology
n Overview
q Introduction
q Chapter 1: Background of compression techniques
q Chapter 2: Multimedia technologies
n JPEG
n MPEG-1/MPEG -2 Audio & Video
n MPEG-4
n MPEG-7 (brief introduction)
n HDTV (brief introduction)
n H261/H263 (brief introduction)
n Model base coding (MBC) (brief introduction)
q Chapter 3: Some real-world systems
n CATV systems
n DVB systems
q Chapter 4: Multimedia Network
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This document introduces multimedia and its key elements. It defines multimedia as a combination of text, graphics, sound, animation and video delivered interactively. The 5 main elements are described as text, audio, graphics, video and animation. It also discusses linear vs non-linear multimedia, authoring tools, importance and applications of multimedia, and different types of multimedia products such as briefing, reference, database, education/training, kiosk and entertainment products.
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This document provides information on the course "IT 2550 Fundamentals of Multimedia (3,2,2)". The course aims to introduce students to the fundamental elements of multimedia through lectures, tutorials, and lab sessions. Students will learn about multimedia representations and applications, and develop skills in multimedia software. The course outcomes include understanding multimedia concepts and technologies, and designing and developing multimedia projects. Upon passing, students will be able to process multimedia data and develop multimedia projects using software tools.
This document contains the notes from a lesson given by Erik Duval on data visualization and analysis. It discusses exploring different types of data, such as graphs, networks, hierarchies, text, and time series. It provides examples of visualization tools and data sources, including links to online courses, open data portals, and visualization examples. It outlines the structure for the lessons, which includes recapping previous work, exploring data, sharing results, and wrapping up. It also asks students about their experiences and solicits feedback on the lessons and tools used.
Secure Distibuted data discovery & dissemination IN WSNSWAMI06
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Efficient Instant-Fuzzy Search With Proximity RankingSWAMI06
System finds answers to a query instantly while user types in keywords character-by-character.
Fuzzy search improves user search experiences by finding relevant answers with keywords similar to query keywords.
A main computational challenge in this paradigm is the high speed requirement
At the same time, we also need good ranking functions that consider the proximity of keywords to compute relevance scores
Multimedia data mining is a popular research domain which helps to extract interesting knowledge from
multimedia data sets such as audio, video, images, graphics, speech, text and combination of several types
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data are stored in multimedia databases and multimedia mining is used to find useful information from
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provides the basic concepts of multimedia mining and its essential characteristics. Multimedia mining
architectures for structured and unstructured data, research issues in multimedia mining, data mining
models used for multimedia mining and applications are also discussed in this paper. It helps the
researchers to get the knowledge about how to do their research in the field of multimedia mining.
Bessel's equation describes functions that arise in various physical problems, such as vibrating membranes and radar. The equation can be solved using an extended power series method to derive Bessel functions of the first kind, which are characterized by their orthogonality properties and represent solutions as a sum of integer powers of x.
Multimedia refers to computer representations of various types of media like audio, video, text and graphics. It allows for integration of different media types that can be stored, transmitted and processed digitally. Multimedia applications combine multiple media sources like text, graphics, images, sound and video. Examples of multimedia applications include the World Wide Web, hypermedia courseware, video conferencing and interactive TV. Effective design of multimedia involves balanced layout, use of white space, consistency and interactivity that gives users control.
The document summarizes 7 principles of multimedia learning from research:
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This document summarizes a technical seminar report on wireless sensor networks submitted by two students, Kapil Dev Dwivedi and Shusma Sandey, to their professor Ravi Ranjan Mishra. The 5-page report includes an abstract, introduction to wireless sensor networks covering their technology, history and architecture, sensor technology, features of WSNs, applications of WSNs including environmental monitoring and health monitoring, standardization, and references.
Multigrade schools were the first type of schools in North America and the Philippines. In the late 1800s, one-room schoolhouses were common in North America before single grade classrooms were organized. Similarly, the earliest schools in the Philippines were multigrade due to factors like remote locations, teacher shortages, and lack of funding. Multigrade classrooms combine two or more grade levels and are used where enrolment does not support single grade classes. They provide an opportunity for student-centered, collaborative learning. The Philippines refers to multigrade classrooms as "combination classes."
It is a technolgy by which we can produce cooling Effect Using MAgnets and Magnetic Materials......
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The document discusses multimedia and its various elements and uses. Multimedia is defined as a combination of text, sound, graphics, video, and animation delivered by computer or electronic media. The key elements of multimedia discussed are text, images, sound, animation, and video. The document also explores the uses of multimedia in business, education, homes, and public places.
Multimedia refers to the integration of multiple mediums of communication like text, graphics, audio, and video. Using multimedia in the classroom can facilitate learning through interactive icebreaker activities and videos or games that enhance language and culture. The advantages of multimedia include students learning more easily and quickly, and having access to entertaining and educational resources, while disadvantages are the need for reliable electricity, potential addictiveness of equipment, and health issues like visual fatigue.
Multimedia is the use of multiple media types such as text, audio, graphics, animation, video and interactivity to convey information. Images are made up of pixels, which are tiny dots that make up the image. The number of pixels and bits per pixel determine image resolution and color depth. Data compression is used to reduce file sizes, and can be either lossless, preserving all image data, or lossy, which discards some data. Common graphic file formats include BMP, GIF and JPEG, which use different compression schemes and are better suited for different types of images. Sound formats include MIDI, MP3, WAV and others that support different capabilities. Multimedia on the web can include animation, streaming audio and
Beyond text qa multimedia answer generation by harvesting web informationJPINFOTECH JAYAPRAKASH
The document proposes a scheme to enrich textual answers from community question answering (cQA) forums with appropriate multimedia data like images and videos. It consists of three components: 1) determining what type of media to add, 2) generating queries to search for multimedia, and 3) selecting and presenting relevant images/videos. This allows cQA to provide more informative answers by supplementing text with visual media. The approach leverages existing human-generated textual answers and focuses on linking those to multimedia, addressing complex questions without requiring deep understanding. It was tested on a multi-source QA dataset and showed effectiveness.
Over the past year Community question answering (cQA) services have Achieved popularity. It allow
members to post and answer questions as enables general users to seek information from comprehensive set of
well-answered questions. But Still, existing cQA forums usually only provide textual answers, for many questions
which are not informative enough. In this paper, we propose a schema that is able to enrich textual answers in
CQA with Appropriate media data. For multimedia search, and multimedia data selection and presentation our
scheme consists of three components: answer medium selection, question base classification ,query generation,
MM data selection and presentation. This method automatically decides which type of media information should
be added for textual answer. It then automatically gathers data from the web. Our approach can enable a novel
multimedia question answering (MMQA) approach by processing large set of QA pairs and adding them to the
pool. users can find multimedia answers by matching their questions with those in a pool. Our approach is based
on community-contributed textual answers and thus it is able to deal with more complex questions.
This document provides an industrial training report on a "Quiz System" project completed at Webtek Labs Pvt. Ltd. It includes sections on the organization profile, introduction of the project, problem specification, objectives, system analysis including feasibility study and hardware/software requirements. The development environment utilized Oracle database for backend and NetBeans IDE 8.1 for frontend development. The report describes testing and implementation of the quiz system including screenshots. It provides an overview of using a computerized quiz system to overcome limitations of manual systems and enable students to take quizzes, view results, and assess learning.
IWE 2480 - An Ecosystem of Innovation: Creating Cognitive Apps Powered by IB...Carmine DiMascio
The document is a keynote presentation about IBM Watson and the IBM Watson ecosystem. Some key points:
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Question Retrieval in Community Question Answering via NON-Negative Matrix Fa...IRJET Journal
The document proposes using statistical machine translation via non-negative matrix factorization to address word ambiguity and mismatch problems in question retrieval for community question answering systems. It translates questions into other languages using Google Translate to leverage contextual information, representing the original and translated questions together in a matrix. Experimental results on a real CQA dataset show this approach improves over methods relying only on surface text matching.
The document discusses predicting the difficulty of keyword queries over databases. It proposes a framework and novel algorithms to assign a robustness score to quantify how difficult it is to retrieve relevant results for a given query over a database. The algorithms aim to efficiently predict the effectiveness of a keyword query based on the robustness principle. Extensive tests show the algorithms can predict query difficulty with relatively low error and minimal overhead.
Enhancing multi-class web video categorization model using machine and deep ...IJECEIAES
With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.
Research: Developing an Interactive Web Information Retrieval and Visualizati...Roman Atachiants
The document describes developing an interactive web information retrieval and visualization system. The system aims to make information searching and presentation easier and more efficient. It does this through speech recognition, keyword extraction from text, query construction and expansion using concepts, filtering and summarizing search results, and visualization. The system architecture includes these main components and was tested with satisfactory results. However, some challenges remain in creating a smooth presentation experience.
The document describes a project submitted by Love Kothari and Mirza Aamir Beag to fulfill the requirements for a Bachelor of Engineering degree in Information Technology at Rajiv Gandhi Prodhyogiki Vishwavidhyalalya, Bhopal, India. The project is titled "NextStep Solution" and was conducted under the guidance of Mr. Deepak Tiwari and Ms. Monika Rawat during the 2016-2017 academic year. The document includes sections on planning, design, implementation, testing and evaluation of the "NextStep Solution" project.
Robotics-Based Learning in the Context of Computer ProgrammingJacob Storer
This document is a project report for research into whether robotics-based learning or simulation-based learning is more effective for teaching programming. It describes the objectives of developing tutorials for both an Arduino robot and visual basic simulation. Programming tasks for moving forwards/backwards and along shapes were developed. Tutorials and programs were implemented to teach these tasks. Surveys were given to test groups after using each method to collect data on their effectiveness for comparison. While results were mixed, all indicated learning was improved with a teacher. Due to the small sample size, no conclusive answer could be provided.
Question Answering has been a well-researched NLP area over recent years. It has become necessary for
users to be able to query through the variety of information available - be it structured or unstructured. In
this paper, we propose a Question Answering module which a) can consume a variety of data formats - a
heterogeneous data pipeline, which ingests data from product manuals, technical data forums, internal
discussion forums, groups, etc. b) addresses practical challenges faced in real-life situations by pointing to
the exact segment of the manual or chat threads which can solve a user query c) provides segments of texts
when deemed relevant, based on user query and business context. Our solution provides a comprehensive
and detailed pipeline that is composed of elaborate data ingestion, data parsing, indexing, and querying
modules. Our solution is capable of handling a plethora of data sources such as text, images, tables,
community forums, and flow charts. Our studies performed on a variety of business-specific datasets
represent the necessity of custom pipelines like the proposed one to solve several real-world document
question-answering
The document describes an intelligent question answering system that can leverage heterogeneous datasets including product manuals, technical forums, and discussion threads. The system includes four main modules: 1) A document parser that can parse different data types including text, images, tables, and forums using deep learning models. 2) A document indexer that indexes documents for retrieval. 3) A document retriever that handles query processing and identifies relevant text segments. 4) A document reader that provides answers by analyzing relevant text segments. The system aims to reduce the time needed to find answers across different data sources by automatically identifying the most relevant information for a given question.
This document describes potential thesis topics in networking that a professor at UNSW is willing to supervise. It provides details on several proposed undergraduate thesis projects related to the supervisor's research interests in areas like video communication, network dependability, and wireless networking. Students are instructed to email their resume and academic record to the professor if interested in any of the topics.
Six Principles of Software Design to Empower ScientistsDavid De Roure
Keynote talk for Workshop on Managing for Usability:
Challenges and Opportunities for E-Science Project Management, 10-11 April 2008,
OeRC, University of Oxford, UK
Enhancing Video Understanding: NLP-Based Automatic Question GenerationIRJET Journal
The document describes a research project that aims to develop an autonomous question generation system to improve video comprehension. The system would use natural language processing techniques to transcribe audio from videos, identify main ideas, and generate questions at different cognitive levels about the video content. This could help students more deeply engage with videos and foster critical thinking skills. The system would combine computer vision to extract visual elements from videos with NLP to transcribe audio, allowing it to develop a comprehensive understanding of video content to generate a wide variety of contextually appropriate questions.
Marking Human Labeled Training Facial Images Searching and Utilizing Annotati...IRJET Journal
This document proposes a system to annotate faces in videos with name labels by matching faces in video frames to a database of labeled facial images. The key steps are:
1) Extract frames from the input video.
2) Recognize faces in each frame and match them to labeled faces in the database to retrieve name annotations.
3) Associate the retrieved names to the corresponding faces in each frame.
Conditional random fields are used to model relationships between detected faces and names to determine the optimal face labeling that maximizes the joint probability. A within-video face labeling algorithm is presented using belief propagation on a constructed graph. Preliminary implementation results demonstrate taking a video as input and extracting its frames.
IRJET - Visual Question Answering – Implementation using KerasIRJET Journal
This document summarizes a research paper that implements visual question answering using Keras deep learning frameworks. The researchers combine image features extracted from VGG-16 with question vectors from spaCy word embeddings to produce answers to questions about images. A multi-layer perceptron is used to merge the CNN and RNN models and produce categorical classes as answers. The implementation uses Keras with TensorFlow backend to extract image features from VGG-16 and question vectors from spaCy, which are then combined in the multi-layer perceptron to answer questions about the images.
1. The study examines how semantic wikis can be used by application service providers (ASPs) as a knowledge management system for managing software artifacts. Semantic wikis offer capabilities like advanced search and collaboration that traditional systems lack.
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3. Evaluating requirements for documentation found semantic wikis can address issues traditional wikis cannot, like understanding architectures. Recommendations include ASPs researching and training on semantic wikis to better manage software artifacts.
IRJET- Review on Intelligent System for CollegeIRJET Journal
This document summarizes a proposed intelligent system for a college campus that uses an Alexa voice assistant and database. The system is designed to make it easy for students, staff, and visitors to find information about the college such as locations of departments, classrooms, and labs or directions around campus. It works by allowing users to ask questions of the Alexa assistant, which then checks the database hosted on a server to respond. The system is meant to reduce the time and effort required for users to find information compared to traditional methods. It provides easy navigation of the college campus and access to basic information.
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at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
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1. 1
RELEVANT MULTIMEDIA QUESTION ANSWERING
A PROJECT REPORT
Submitted by
PRASANTH.G - 100105901019
SURYA.B - 100105901034
VEMBARASU.L - 100105901039
in partial fulfillment for the award of the degree
of
BACHELOR OF ENGINEERING
IN
COMPUTER SCIENCE AND ENGINEERING
DR.NALLINI INSTITUTE OF ENGINEERING AND TECNOLOGY
DHARAPURAM-638 673
ANNA UNIVERSITY OF TECHNOLOGY::CHENNAI-600 025
APRIL 2014
2. 2
ANNA UNIVERSITY OF TECHNOLOGY:CHENNAI-600 025
BONAFIDE CERTIFICATE
Certified that this project report “RELEVANT MULTIMEDIA QUESTION
ANSWER” is the bonafide work of “PRASANTH.G, SURYA.B,
VEMBARASU.L” Who carried out the project work under my supervision.
SIGNATURE SIGNATURE
Mr.P.MANIVANNAN M.TECH., Mr.S.ANANDHASARAVANAN ME.,
HEAD OF THE DEPARTMENT SUPERVISOR
Department Of Computer Science Department Of Computer Science
Engineering Engineering
Dr.Nallini Institute Of Engineering Dr.Nallini Institute Of Engineering
and Tecnology and Tecnology
Dharapuram – 638 673 Dharapuram – 638 673
Submitted To University Viva Examination Held On……………………………
INTERNAL EXAMINER EXTERNAL EXAMINER
3. 3
ACKNOWLEDGEMENT
First of all i thank god almighty for all his blessings our heartfelt
thankfulness goes to our honorable chairman Dr.S.APPUSWAMY,
M.S.,PH.D.,F.I.C.M.,M.R.S.H.(LOND) for having provided us with the
entire necessary infra structure and other facilities for his extensive support to
successfully carry out this project.
I extend our gratitude to Dr.E.RAMASAMY, M.TECH.,Ph.D, principal,
Dr.Nallini institute of engineering &technolongy,Dharapuram, for his high degree
of encouragement and moral support during the course of this project.
I extremely happy for expressing our heartfelt gratitude to
Mr.P.MANIVANNAN M.TECH.,head of the department ,department of
computer science engineering, Dr.Nallini institute of
engineering&technology,Dharapuram, for extending all possible works and also
his valuable guidance in making this project a grand success.
I extent our honest gratitude to Mr.S.ANANDHASARAVANAN
M.E.,department of computer science engineering ,Dr.Nallini institute of
engineering & technology, Dharapuram.
I thank all teaching & non teaching staffs of computer science engineering
department who have helped us during the course of our project.
I am also express gratitude towards our parents and friends for their valuable help.
4. 4
ABSTRACT
In general, while searching in search engine one would search for an exact
answer for his query. In the existing QA forums, it usually provide only textual
answers which are not informative enough for real queries. In this paper, we
propose the scheme that is able to enrich the textual answers with appropriate
media data. By processing a large set of question answer, this approach can enable
a multimedia question answering(MMQA). MMQA research offers that attempt to
directly answer questions with image and video data. In my approach, we propose
to contribute textual answers and able to deal with more complex queries.To
enhance the above said process, propose a scheme named “WSMA” to enrich text
answers with image & video and able to take relevance and diversity into account
by exploring the content retreival.
5. 5
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT I
LIST OF TABLES Iv
LIST OF FIGURES V
LIST OF ABBREVIATIONS Vi
1 INTRODUCTION 01
2 LITERATURE SURVEYS 04
2.1Multimedia Answering: Enriching Text
QA with Media Information
04
2.2Knowledge Sharing and Yahoo Answers:
Everyone Knows Something
04
2.3Automatic Set Expansion for List
Question Answering
05
2.4Semi-supervised kernel density estimation
for video annotation
05
2.5Photo-based Question Answering 06
3 SYSTEM ANALYSIS 07
3.1 Existing System 07
3.2 Proposed System 08
7. 7
LIST OF TABLES
TABLE NO TITLE PAGE NO
3.1 Sample questions answerable by template-based QA
layer
09
3.2 Illustration of QA Event Elements 10
8. 8
LIST OF FIGURES
TABLE NO TITLE PAGE
NO
3.1 Images In Our Pilot Dataset 09
3.2 Illustration of QA Event Elements 10
4.1 Stateless XML Web services model 15
4.2 .net Architecture 16
4.3 Inside The Common Language Runtime 19
4.4 Visual Basic compiler options dialog 20
4.5 The JIT process and verification 21
4.6 The .NET Framework class library. 22
5.1 Architectural Diagram 35
9. 9
LIST OF ABBREVIATIONS
MLMIL Multilabel Multi-Instance Learning
HCRFs Hidden Conditional Random Fields
SSKDE Semisupervised Kernel Density Estimation
KDE Kernel Density Estimation
SSAKDE Semi-Supervised Adaptive Kernel Density Estimation
MMQA Multimedia Question Answering
SMTP Simple Mail Transfer Protocol
SOAP Simple Object Access Protocol
CIL Common Intermediate Language
GUI Graphical User Interface
ECMA European Computer Manufacturers Association
10. 10
CHAPTER 1
INTRODUCTION
Question-answering (qa) is a technique for automatically answering a
question posed in natural language compared to keyword-based search systems, it
greatly facilitates the communication between humans and computers by naturally
stating users’ intention in plain sentences. It also avoids the painstaking browsing
of a vast quantity of information contents returned by search engines for the correct
answers. However, fully automated QA still faces challenges that are not easy to
tackle, such as the deep understanding of complex questions and the sophisticated
syntactic, semantic and contextual processing to generate answers. It is found that,
in most cases, automated approach cannot obtain results that are as good as those
generated by human intelligence. Along with the proliferation and improvement of
underlying communication technologies, community QA (cQA) has emerged as an
extremely popular alternative to acquire information online, owning to the
following facts.
First, information seekers are able to post their specific questions on any
topic and obtain answers provided by other participants. By leveraging community
efforts, they are able to get better answers than simply using search engines.
Second, in comparison with automated QA systems, cQA usually receives answers
with better quality as they are generated based on human intelligence. Third, over
times, a tremendous number of QA pairs have been accumulated in their
repositories, and it facilitates the preservation and search of answered questions.
11. 11
For example, Wiki Answer, one of the most well-known cQA systems, hosts more
than 13 million answered questions distributed in 7,000
categories. Despite their great success, existing cQA forums mostly support
only textual answers. Unfortunately, textual answers may not provide sufficient
natural and easy-to grasp information. For the questions “What are the steps to
make a weather vane” and “What does $1 Trillion Look Like”, the answers are
described by long sentences. Clearly, it will be much better if there are some
accompanying videos and images that visually demonstrate the process or the
object. Therefore, the textual answers in cQA can be significantly enhanced by
adding multimedia contents, and it will provide answer seekers more
comprehensive information and better experience.
In fact, users usually post URLs that link to supplementary images or videos
in their textual answers. For example, for the questions , the best answers on Y!A
both contain video URLs. It further confirms that multimedia contents are useful in
answering several questions. But existing cQA forums do not provide adequate
support in using media information.
In this paper, we propose a novel scheme which can enrich community-contributed
textual answers in cQA with appropriate media data. It contains three main
components:
(1) Answer medium selection. Given a QA pair, it predicts whether the textual
answer should be enriched with media information, and which kind of media data
should be added. Specifically, we will categorize it into one of the four classes. It
means that the scheme will automatically collect images, videos, or the
combination of images and videos to enrich the original textual answers.
12. 12
(2) Query generation for multimedia search. In order to collect multimedia data,
we need to generate informative queries. Given a QA pair, this component extracts
three queries from the question, the answer, and the QA pair, respectively. The
most informative query will be selected by a three-class classification model.
(3) Multimedia data selection and presentation. Based on the generated queries, we
vertically collect image and video data with multimedia search engines. We then
perform reranking and duplicate removal to obtain a set of accurate and
representative images or videos to enrich the textual answers.
13. 13
CHAPTER 2
LITERARURE SURVEY
2.1 Multimedia Answering: Enriching Text QA with Media Information:
In this paper, we introduce a scheme that is able to enrich text answers with
image and video information. Our scheme investigates a rich set of techniques
including question/answer classification, query generation, image and video search
reranking, etc. Given a question and the community-contributed answer, our
approach is able to determine which type of media information should be added,
and then automatically collects data from Internet to enrich the textual answer.
Different from some efforts that attempt to directly answer questions with image
and video data, our approach is built based on the community-contributed textual
answers and thus it is more feasible and able to deal with more complex questions.
We have conducted empirical study on more than 3,000 QA pairs and the results
demonstrate the effectiveness of our approach.
2.2 Knowledge Sharing and Yahoo Answers: Everyone Knows Something
In this paper, we seek to understand YA’s knowledge sharing activity. We
analyze the forum categories and cluster them according to content characteristics
and patterns of interaction among the users. While interactions in some categories
resemble expertise sharing forums, others incorporate discussion, everyday advice,
and support. With such a diversity of categories in which one can participate, we
find that some users focus narrowly on specific topics, while others participate
across categories. This not only allows us to map related categories, but to
characterize the entropy of the users’ interests. We find that lower entropy
correlates with receiving higher answer ratings, but only for categories where
14. 14
factual expertise is primarily sought after. We combine both user attributes and
answer characteristics to predict, within a given category, whether a particular
answer will be chosen as the best answer by the asker.
2.3 Automatic Set Expansion for List Question Answering
This paper explores the use of set expansion (SE) to improve question
answering (QA) when the expected answer is a list of entities belonging to a
certain class. Given a small set of seeds, SE algorithms mine textual resources to
produce an extended list including additional members of the class represented by
the seeds. We explore the hypothesis that a noise-resistant SE algorithm can be
used to extend candidate answers produced by a QA system and generate a new list
of answers that is better than the original list produced by the QA system. We
further introduce a hybrid approach which combines the original answers from the
QA system with the output from the SE algorithm. Experimental results for
severalstate-of-the-art QA systems show that the hybrid system performs better
than the QA systems alone when tested on list question data from past TREC
evaluations.
2.4 Semi-supervised kernel density estimation for video annotation
In this paper, we propose a novel semi-supervised learning algorithm named
semisupervised kernel density estimation (SSKDE) which is developed based on
kernel density estimation (KDE) approach. While only labeled data are utilized in
classical KDE, in SSKDE both labeled and unlabeled data are leveraged to
estimate class conditional probability densities based on an extended form of KDE.
It is a non-parametric method, and it thus naturally avoids the model assumption
problem that exists in many parametric semi-supervised methods. Meanwhile, it
can be implemented with an efficient iterative solution process. So, this method is
15. 15
appropriate for video annotation. Furthermore, motivated by existing adaptive
KDE approach, we propose an improved algorithm named semi-supervised
adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels
rather than a fixed kernel, such that broader kernels can be applied in the regions
with low density. In this way, more accurate density estimates can be obtained.
Extensive experiments have demonstrated the effectiveness of the proposed
methods.
2.5 Photo-based Question Answering
A photo-based QA system allows direct use of a photo to refer to the object.
We develop a three-layer system architecture for photo-based QA that brings
together recent technical achievements in question answering and image matching.
The first, template-based QA layer matches a query photo to online images and
extracts structured data from multimedia databases to answer questions about the
photo. To simplify image matching, it exploits the question text to filter images
based on categories and keywords. The second, information retrieval QA layer
searches an internal repository of resolved photo-based questions to retrieve
relevant answers. The third, human-computation QA layer leverages community
experts to handle the most difficult cases. A series of experiments performed on a
pilot dataset of 30,000 images of books, movie DVD covers, grocery items, and
landmarks demonstrate the technical feasibility of this architecture. We present
three prototypes to show how photo-based QA can be built into an online album,
a text-based QA, and a mobile application.
16. 16
CHAPTER 3
SYSTEM ANALYSIS
3.1 EXISTING SYSTEM:
• In existing, we propose a scheme that is able to enrich textual answers in
cQA with appropriate media data.
• Increasing exposure by the use of web harvesting is essential for a business
wishing to expand online.
• It then automatically collects data from the web to enrich the answer.
• By processing a large set of QA pairs and adding them to a pool, our
approach can enable a novel multimedia question answering (MMQA)
approach as users can find multimedia answers by matching their questions
with those in the pool.
• No content retreival.
.
ISSUES IN EXISTING SYSTEM:
• The system may fail to generate reasonable multimedia answers if the
generated queries are verbose and complex.
• Relevant and irrelevant datas will generate
• Non automated maintenance
17. 17
3.2 PROPOSED SYSTEM:
• We proposes a relevance reranking scheme which is able to simultaneously
take relevance data and diversity into account.
• It takes advantage of both the content of images and their associated
multimedia contents.
• It estimates the relevance scores of images with respect to the query term based
on both the visual information, images and the semantic information of
associated multimedia datas.
• Here, we propose “WSMA” to enrich text answers with image & video and able
to take relevance and diversity into account by exploring the content of images.
It investigates a rich set of techniques :
1) Question/Answer classification.
2) Query generation.
3) image and video search re-ranking.
18. 18
FOR EXAMPLES:
TABLE 3.1 Sample questions answerable by the
template-based QA layer.
Fig 3.1 Images in our pilot dataset.
CATEGORY SAMPLE QUESTIONS
ALL What is it?
PRODUCT
Is it in stock?
How much is this on Amazon?
Where can I buy it?
ENTERTAINMENT
What is its rating?
What is its review?
Is there a sequel?
MOVIE
Is there a blue-ray edition?
What is its boxoffice?
Who is the director?
BOOK
Is it a fiction?
Is there a paperback edition?
Who is the author?
LANDMARK
Where is it?
Who is the architect?
When was it built?
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WH-Question QA Event Elements
Who/Whose/Whom Subject, Object
Where Location
When Time
What Subject, Object, Description, Action
Which Subject, Object
How Quantity, Description
Table 3.2 Correspondence of WH-Questions & Event Elements
Figure 3.2 Illustration of QA Event Elements
20. 20
ADVANTAGES:
The benefits from solving the above challenge would be enormous. Potential
applications include 1) a formalized machine-readable encyclopedia that can be
queried with high precision like a semantic database; 2) a key asset for
disambiguating entities by supporting fast and accurate mappings of textual
phrases onto named entities in the knowledge base; 3) an enabler for entity-
relation- ship-oriented semantic search on the Web, for detecting entities and
relations in Web pages and reasoning about them in expressive (probabilistic)
logics; 4) a backbone for natural-language question answering that would aid in
dealing with entities and their relationships in answering who/where/when/ etc.
questions; 5) a catalyst for acquisition of further knowledge and largely automated
maintenance and growth of the knowledge base
21. 21
CHAPTER 4
SOFTWARE DESCRIPTION
SOFTWARE REQUIREMENTS:
Operating system : Windows XP Professional
Environment : Visual Studio .NET 2008
.Net framework : Version 3.5
Language : C#.NET
Web technology : ASP.NET
Backend : SQL SERVER 2005
HARDWARE REQUIREMENTS
PROCESSOR : PENTIUM III 866 MHz
RAM : 128 MD SD RAM
MONITOR : 15” COLOR
HARD DISK : 20 GB
FLOPPY DRIVE : 1.44 MB
CD DRIVE : LG 52X
KEYBOARD : STANDARD 102 KEYS
MOUSE : 3 BUTTONS
22. 22
SOFTWARE SPECIFICATION
• Microsoft C#. Net used as front end tool. The reason for selecting C# dot
Net as front end tool as follows:
• C#. Net has flexibility , allowing one or more language to interoperate to
provide the solution. This Cross Language Compatibility allows to do
project at faster rate.
• C#. Net has Common Language Runtime , that allows all the component to
converge into one intermediate format and then can interact.
• C#. Net has provide excellent security when your application is executed in
the system
• C#.Net has flexibility, allowing us to configure the working environment to
best suit our individual style. We can choose between a single and multiple
document interfaces, and we can adjust the size and positioning of the
various IDE elements.
• C#. Net has Intelligence feature that make the coding easy and also
Dynamic help provides very less coding time.
• The working environment in C#.Net is often referred to as Integrated
Development Environment because it integrates many different functions
such as design, editing, compiling and debugging within a common
environment. In most traditional development tools, each of separate
program, each with its own interface.
• The C#.Net language is quite powerful – if we can imagine a programming
task and accomplished using C#.Net.
23. 23
• After creating a C#. Net application, if we want to distribute it to others we
can freely distribute any application to anyone who uses Microsoft windows.
We can distribute our applications on disk, on CDs, across networks, or over
an intranet or the internet.
• Toolbars provide quick access to commonly used commands in the
programming environment. We click a button on the toolbar once to carry
out the action represented by that button. By default, the standard toolbar is
displayed when we start Visual Basic. Additional toolbars for editing, form
design, and debugging can be toggled on or off from the toolbars command
on the view menu.
• Many parts of C# are context sensitive. Context sensitive means we can get
help on these parts directly without having to go through the help menu. For
example, to get help on any keyword in the C#language, place the insertion
point on that keyword in the code window and press F1.
• C# interprets our code as we enter it, catching and highlighting most syntax
or spelling errors on the fly. It’s almost like having an expert watching over
our shoulder as we enter our code.
FEATURES OF DOTNET
When .NET was announced in late 1999, Microsoft positioned the
technology as a platform for building and consuming Extensible Markup Language
(XML) Web services. XML Web services allow any type of application, be it a
Windows- or browser-based application running on any type of computer system,
to consume data from any type of server over the Internet. The reason this idea is
so great is the way in which the XML messages are transferred: over established
24. 24
standard protocols that exist today. Using protocols such as SOAP, HTTP, and
SMTP, XML Web services make it possible to expose data over the wire with little
or no modifications to your existing code. Figure 4.1 presents a high-level
overview of the .NET Framework and how XML Web services are positioned.
Fig 4.1
Stateless XML Web services model.
Since the initial announcement of the .NET Framework, it's taken on many new
and different meanings to different people. To a developer, .NET means a great
environment for creating robust distributed applications. To an IT manager, .NET
means simpler deployment of applications to end users, tighter security, and
simpler management. To a CTO or CIO, .NET means happier developers using
state-of-the-art development technologies and a smaller bottom line. To understand
why all these statements are true, you need to get a grip on what the .NET
Framework consists of, and how it's truly a revolutionary step forward for
application architecture, development, and deployment.
25. 25
.NET FRAMEWORK
Now that you are familiar with the major goals of the .NET Framework,
let's briefly examine its architecture. As you can see in Figure 3-2, the .NET
Framework sits on top of the operating system, which can be a few different
flavors of Windows and consists of a number of components .NET is essentially a
system application that runs on Windows. Conceptually, the CLR and the JVM are
similar in that they are both runtime infrastructures that abstract the underlying
platform differences. However, while the JVM officially supports only the Java
language, the CLR supports any language that can be represented in its Common
Intermediate Language (CIL).
Fig 4.2 .net Architecture
The JVM executes byte code, so it can, in principle, support many
languages, too. Unlike Java's byte code, though, CIL is never interpreted. Another
conceptual difference between the two infrastructures is that Java code runs on any
platform with a JVM, whereas .NET code runs only on platforms that support the
CLR. In April, 2003, the International Organization for Standardization and the
International Electro technical Committee (ISO/IEC) recognized a functional
26. 26
subset of the CLR, known as the Common Language Interface (CLI), as an
international standard.
This development, initiated by Microsoft and developed by ECMA
International, a European standards organization, opens the way for third parties to
implement their own versions of the CLR on other platforms, such as Linux or
Mac OS X. For information on third-party and open source projects working to
implement the ISO/IEC CLI and C# specifications. The layer on top of the CLR is
a set of framework base classes. This set of classes is similar to the set of classes
found in STL, MFC, ATL, or Java. These classes support rudimentary input and
output functionality, string manipulation, security management, network
communications, thread management, text management, reflection functionality,
collections functionality, as well as other functions.
On top of the framework base classes is a set of classes that extend
the base classes to support data management and XML manipulation. These
classes, called ADO.NET, support persistent data management—data that is stored
on backend databases. Alongside the data classes, the .NET Framework supports a
number of classes to let you manipulate XML data and perform XML searching
and XML translations .Classes in three different technologies (including web
services, Web Forms, and Windows Forms) extend the framework base classes and
the data and XML classes.
Web services include a number of classes that support the
development of lightweight distributed components, which work even in the face
of firewalls and NAT software. These components support plug-and-play across
the Internet, because web services employ standard HTTP and SOAP. Web Forms,
27. 27
the key technology behind ASP.NET, include a number of classes that allow you to
rapidly develop web Graphical User Interface (GUI) applications.
If you're currently developing web applications with Visual Interdev, you
can think of Web Forms as a facility that allows you to develop web GUIs using
the same drag-and-drop approach as if you were developing the GUIs in Visual
Basic. Simply drag-and-drop controls onto your Web Form, double-click on a
control, and write the code to respond to the associated event. Forms support a set
of classes that allow you to develop native Windows GUI applications. You can
think of these classes collectively as a much better version of the MFC in C++
because they support easier and more powerful GUI development and provide a
common, consistent interface that can be used in all languages.
THE COMMON LANGUAGE RUNTIME:
At the heart of the .NET Framework is the common language runtime. The
common language runtime is responsible for providing the execution environment
that code written in a .NET language runs under. The common language runtime
can be compared to the Visual Basic 6 runtime, except that the common language
runtime is designed to handle all .NET languages, not just one, as the Visual Basic
6 runtime did for Visual Basic 6. The following list describes some of the benefits
the common language runtime gives you
• Automatic memory management
• Cross-language debugging
• Cross-language exception handling
• Full support for component versioning
• Access to legacy COM components
28. 28
• XCOPY deployments
• Robust security model
You might expect all those features, but this has never been possible using
Microsoft development tools. Figure 10.3 shows where the common language
runtime fits into the .NET Framework.
The common language runtime and the .NET Framework.
Fig 4.3 INSIDE THE COMMON LANGUAGE RUNTIME
The common language runtime enables code running in its execution
environment to have features such as security, versioning, memory management
and exception handling because of the way .NET code actually executes. When
you compiled Visual Basic 6 forms applications, you had the ability to compile
down to native node or p-code. Figure 3.4 should refresh your memory of what the
Visual Basic 6 options dialog looked like.
29. 29
Fig 4.4 Visual Basic compiler options dialog.
When you compile in .NET, you're converting your code—no matter what
.NET language you're using—into an assembly made up of an intermediate
language called Microsoft Intermediate Language (MSIL or just IL, for short). The
IL contains all the information about your application, including methods,
properties, events, types, exceptions, security objects, and so on, and it also
includes metadata about what types in your code can or cannot be exposed to other
applications. This was called a type library in Visual Basic 6 or an IDL (interface
definition language) file in C++. In .NET, it's simply the metadata that the IL
contains about your assembly.
30. 30
Fig 4.5 The JIT process and verification.
When code is JIT compiled, the common language runtime checks to make sure
that the IL is correct. The rules that the common language runtime uses for
verification are set forth in the Common Language Specification (CLS) and the
Common Type System (CTS).
THE .NET FRAMEWORK CLASS LIBRARY
The second most important piece of the .NET Framework is the .NET
Framework class library (FCL). As you've seen, the common language runtime
handles the dirty work of actually running the code you write. But to write the
code, you need a foundation of available classes to access the resources of the
operating system, database server, or file server. The FCL is made up of a
hierarchy of namespaces that expose classes, structures, interfaces, enumerations,
and delegates that give you access to these resources. The namespaces are logically
defined by functionality. For example, the System. Data namespace contains all the
functionality available to accessing databases.
31. 31
This namespace is further broken down into System.Data.SqlClient, which
exposes functionality specific to SQL Server, and System.Data.OleDb, which
exposes specific functionality for accessing OLEDB data sources. The bounds of a
namespace aren't necessarily defined by specific assemblies within the FCL; rather,
they're focused on functionality and logical grouping. In total, there are more than
20,000 classes in the FCL, all logically grouped in a hierarchical manner. Figure
1.8 shows where the FCL fits into the .NET Framework and the logical grouping
of namespaces.
Fig 4.6 the .NET Framework class library.
To use an FCL class in your application, you use the Imports statement
in Visual Basic .NET or the using statement in C#. When you reference a
namespace in Visual Basic .NET or C#, you also get the convenience of auto-
complete and auto-list members when you access the objects' types using Visual
Studio .NET. This makes it very easy to determine what types are available for
each class in the namespace you're using. As you'll see over the next several
weeks, it's very easy to start coding in Visual Studio .NET.
32. 32
THE STRUCTURE OF A .NET APPLICATION
To understand how the common language runtime manages code execution, you
must examine the structure of a .NET application. The primary unit of a .NET
application is the assembly. An assembly is a self-describing collection of code,
resources, and metadata. The assembly manifest contains information about what
is contained within the assembly. The assembly manifest provides:
• Identity information, such as the assembly’s name and version number
• A list of all types exposed by the assembly
• A list of other assemblies required by the assembly
• A list of code access security instructions, including permissions required by
the assembly and permissions to be denied the assembly
Each assembly has one and only one assembly manifest, and it contains
all the description information for the assembly. However, the assembly manifest
can be contained in its own file or within one of the assembly’s modules. An
assembly contains one or more modules. A module contains the code that makes
up your application or library, and it contains metadata that describes that code.
When you compile a project into an assembly, your code is converted
from high-level code to IL. Because all managed code is first converted to IL code,
applications written in different languages can easily interact. For example, one
developer might write an application in Visual C# that accesses a DLL in Visual
Basic .NET. Both resources will be converted to IL modules before being
executed, thus avoiding any language-incompatibility issues. Each module also
contains a number of types.
33. 33
Types are templates that describe a set of data encapsulation and
functionality. There are two kinds of types: reference types (classes) and value
types (structures). These types are discussed in greater detail in Lesson 2 of this
chapter. Each type is described to the common language runtime in the assembly
manifest. A type can contain fields, properties, and methods, each of which should
be related to a common functionality. For example, you might have a class that
represents a bank account. It contains fields, properties, and methods related to the
functions needed to implement a bank account. A field represents storage of a
particular type of data. One field might store the name of an account holder, for
example. Properties are similar to fields, but properties usually provide some kind
of validation when data is set or retrieved. You might have a property that
represents an account balance.
When an attempt is made to change the value, the property can check
to see if the attempted change is greater than a predetermined limit. If the value is
greater than the limit, the property does not allow the change. Methods represent
behavior, such as actions taken on data stored within the class or changes to the
user interface. Continuing with the bank account example, you might have a
Transfer method that transfers a balance from a checking account to a savings
account, or an Alert method that warns users when their balances fall below a
predetermined level.
INTRODUCTION TO OBJECT-ORIENTED PROGRAMMING
Programming in the .NET Framework environment is done with objects.
Objects are programmatic constructs that represent packages of related data and
functionality. Objects are self-contained and expose specific functionality to the
34. 34
rest of the application environment without detailing the inner workings of the
object itself. Objects are created from a template called a class. The .NET base
class library provides a set of classes from which you can create objects in your
applications. You also can use the Microsoft Visual Studio programming
environment to create your own classes. This lesson introduces you to the concepts
associated with object-oriented programming.
OVERVIEW OF ADO.NET
Most applications require some kind of data access. Desktop applications
need to integrate with central databases, Extensible Markup Language (XML) data
stores, or local desktop databases. ADO.NET data-access technology allows
simple, powerful data access while maximizing system resource usage. Different
applications have different requirements for data access. Whether your application
simply displays the contents of a table, or processes and updates data to a central
SQL server, ADO.NET provides the tools to implement data access easily and
efficiently.
DISCONNECTED DATABASE ACCESS
Previous data-access technologies provided continuously connected data
access by default. In such a model, an application creates a connection to a
database and keeps the connection open for the life of the application, or at least
for the amount of time that data is required. However, as applications become more
complex and databases serve more and more clients, connected data access is
impractical for a variety of reasons, including the following:
35. 35
• Open database connections are expensive in terms of system resources. The
more open connections there are, the less efficient system performance
becomes.
• Applications with connected data access are difficult to scale. An application
that can comfortably maintain connections with two clients might do poorly
with 10 and be completely unusable with 100.
• Open database connections can quickly consume all available database licenses,
which can be a significant expense. In order to work within a limited set of
client licenses, connections must be reused whenever possible.
ADO.NET addresses these issues by implementing a disconnected data
access model by default. In this model, data connections are established and left
open only long enough to perform the requisite action. For example, if an
application requests data from a database, the connection opens just long enough to
load the data into the application, and then it closes. Likewise, if a database is
updated, the connection opens to execute the UPDATE command, and then closes
again.
By keeping connections open only for the minimum required time, ADO.NET
conserves system resources and allows data access to scale up with a minimal
impact on performance.
ADO.NET DATA ARCHITECTURE
Data access in ADO.NET relies on two entities: the Dataset, which stores
data on the local machine, and the Data Provider, a set of components that
mediates interaction between the program and the database.
36. 36
The DATASET
The Dataset is a disconnected, in-memory representation of data. It can be
thought of as a local copy of the relevant portions of a database. Data can be
loaded into a Dataset from any valid data source, such as a SQL Server database, a
Microsoft Access database, or an XML file. The Dataset persists in memory, and
the data therein can be manipulated and updated independent of the database.
When appropriate, the Dataset can then act as a template for updating the central
database.
The Dataset object contains a collection of zero or more Data Table objects,
each of which is an in-memory representation of a single table. The structure of a
particular Data Table is defined by the Data Columns collection, which enumerates
the columns in a particular table, and the Constraint collection, which enumerates
any constraints on the table. Together, these two collections make up the table
schema. A Data Table also contains a Data Rows collection, which contains the
actual data in the Dataset.
The Dataset contains a Data Relations collection. A Data Relation object
allows you to create associations between rows in one table and rows in another
table. The Data Relations collection enumerates a set of Data Relation objects that
define the relationships between tables in the Dataset. For example, consider a
Dataset that contains two related tables: an Employees table and a Projects table. In
the Employees table, each employee is represented only once and is identified by a
unique Employee field. In the Projects table, an employee in charge of a project is
identified by the Employee field, but can appear more than once if that employee is
in charge of multiple projects. This is an example of a one-to-many relationship;
37. 37
you would use a Data Relation object to define this relationship. Additionally, a
Dataset contains an Extended Properties collection, which is used to store custom
information about the Dataset.
THE DATA PROVIDER
The link to the database is created and maintained by a data provider. A data
provider is not a single component; rather it is a set of related components that
work together to provide data in an efficient, performance-driven manner. The first
version of the Microsoft .NET Framework shipped with two data providers: the
SQL Server .NET Data Provider, designed specifically to work with SQL Server 7
or later, and the Loeb .NET Data Provider, which connects with other types of
databases. Microsoft Visual Studio .NET 2005 added two more data providers: the
ODBC Data Provider and the Oracle Data Provider. Each data provider consists of
versions of the following generic component classes:
• The Connection object provides the connection to the database.
• The Command object executes a command against a data source. It can
execute non-query commands, such as INSERT, UPDATE, or DELETE, or
return a Data Reader with the results of a SELECT command.
• The Data Reader object provides a forward-only, read-only, connected
record set.
• The Data Adapter object populates a disconnected Dataset or Data Table
with data and performs updates.
Data access in ADO.NET is facilitated as follows: a Connection object
establishes a connection between the application and the database. This connection
38. 38
can be accessed directly by a Command object or by a Data Adapter object. The
Command object provides direct execution of a command to the database. If the
command returns more than a single value, the Command object returns a Data
Reader to provide the data. This data can be directly processed by application
logic. Alternatively, you can use the Data Adapter to fill a Dataset object. Updates
to the database can be achieved through the Command object or through the Data
Adapter. The generic classes that make up the data providers are summarized in
the following sections.
THE CONNECTION OBJECT
The Connection object represents the actual connection to the database.
Visual Studio .NET 2003 supplies two types of Connection classes: the
SqlConnection object, which is designed specifically to connect to SQL Server 7 or
later, and the OleDbConnection object, which can provide connections to a wide
range of database types. Visual Studio .NET 2003 further provides a multipurpose
ODBCConnection class, as well as an Oracle Connection class optimized for
connecting to Oracle databases. The Connection object contains all of the
information required to open a channel to the database in the Connection String
property. The Connection object also incorporates methods that facilitate data
transactions.
THE COMMAND OBJECT
The Command object is represented by two corresponding classes,
SqlCommand and OleDbCommand. You can use Command objects to execute
commands to a database across a data connection. Command objects can be used
to execute stored procedures on the database and SQL commands, or return
39. 39
complete tables. Command objects provide three methods that are used to execute
commands on the database.
• EXECUTENONQUERY
Executes commands that return no records, such as INSERT, UPDATE, or
DELETE
• EXECUTESCALAR
Returns a single value from a database query
• EXECUTEREADER
Returns a result set by way of a DataReader object
THE DATA READER OBJECT
The Data Reader object provides a forward-only, read-only, connected
stream record set from a database. Unlike other components of a data provider,
Data Reader objects cannot be directly instantiated. Rather, the Data Reader is
returned as the result of a Command object’s ExecuteReader method. The
SqlCommand.ExecuteReader method returns a SqlDataReader object, and the
OleDbCommand.ExecuteReader method returns an OleDbDataReader object.
Likewise, the ODBC and Oracle Command.ExecuteReader methods return a
DataReader specific to the ODBC and Oracle Data Providers respectively. The
DataReader can supply rows of data directly to application logic when you do not
need to keep the data cached in memory.
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THE DATAADAPTER OBJECT
The DataAdapter is the class at the core of ADO.NET disconnected data
access. It is essentially the middleman, facilitating all communication between the
database and a DataSet.The DataAdapter provides four properties that represent
database commands. The four properties are:
• SELECT COMMAND
Contains the command text or object that selects the data from the database.
This command is executed when the Fill method is called and fills a Data
Table or a DataSet.
• INSERT COMMAND
Contains the command text or object that inserts a row into a table.
• DELETE COMMAND
Contains the command text or object that deletes a row from a table.
• UPDATE COMMAND
Contains the command text or object that updates the values of a
database.
When the Update method is called, changes in the DataSet are copied back to the
database, and the appropriate Insert Command, Delete Command, or Update
Command is executed.
41. 41
ACCESSING DATA
Visual Studio .NET has many built-in wizards and designers to help you
shape your data-access architecture rapidly and efficiently. With minimal actual
coding, you can implement robust data access for your application. However, the
ADO.NET object model is fully available through code to implement customized
features or to fine-tune your program.
FEATURES OF SQL SERVER
The OLAP Services feature available in SQL Server version 7.0 is now called
SQL Server 2000 Analysis Services. The term OLAP Services has been replaced
with the term Analysis Services. Analysis Services also includes a new data mining
component. The Repository component available in SQL Server version 7.0 is now
called Microsoft SQL Server 2000 Meta Data Services. References to the
component now use the term Meta Data Services.
SQL-SERVER database consist of six type of objects, They are,
1. TABLE
2. QUERY
3. FORM
4. REPORT
5. MACRO
6. MODULE
42. 42
TABLE:
A database is a collection of data about a specific topic.
VIEWS OF TABLE:
We can work with a table in two types,
1. Design View
2. Datasheet View
QUERY:
A query is a question that has to be asked the data. Access gathers data that
answers the question from one or more table. The data that make up the answer
is either dynaset (if you edit it) or a snapshot (it cannot be edited).Each time we
run query, we get latest information in the dynaset. Access either displays the
dynaset or snapshot for us to view or perform an action on it, such as deleting or
updating.
FORMS:
A form is used to view and edit information in the database record by
record .A form displays only the information we want to see in the way we
want to see it. Forms use the familiar controls such as textboxes and
checkboxes. This makes viewing and entering data easy.
43. 43
VIEWS OF FORM:
We can work with forms in several primarily there are two views,
They are,
• Design View
• Form View
DESIGN VIEW:
To build or modify the structure of a form, we work in forms design view.
We can add control to the form that are bound to fields in a table or query,
includes textboxes, option buttons, graphs and pictures.
FORM VIEW:
The form view which display the whole design of the form.
REPORT:
A report is used to views and print information from the database. The
report can ground records into many levels and compute totals and average by
checking values from many records at once. Also the report is attractive and
distinctive because we have control over the size and appearance of it.
MACRO:
A macro is a set of actions. Each action in macros does something. Such as
opening a form or printing a report .We write macros to automate the common
tasks the work easy and save the time.
MODULE:
Modules are units of code written in access basic language. We can write and
use module to automate and customize the database in very sophisticated ways.
46. 46
5.2 ALGORITHM
Relevance ranking algorithm:
There exist multiple variations of neighborhood-based CF
techniques. In this paper, to estimate R*(u, i), i.e., the rating that user u would give
to item i, we first compute the similarity between user u and other users u' using a
cosine similarity metric.
Formula : for finding prediction by using recommendation techniques:
Sim(u,u’)=sum[R(u,i).R(u’,i)]/sqrt[sum[R(u,i)2].sqrt[sum[R(u’,i)]
Where I (u, u') represents the set of all items rated by both user u and user u'. Based
on the similarity calculation, set N (u) of nearest neighbors of user u is obtained.
The size of set N (u) can range anywhere from 1 to |U|-1, i.e., all other users in the
dataset.
Then, R*(u, i) is calculated as the adjusted weighted sum of all
known ratings R (u', i) Here R (u) represents the average rating of user u. A
neighborhood-based CF technique can be user-based or item-based, depending on
whether the similarity is calculated between users or items, the user-based
approach, but they can be straightforwardly rewritten for the item-based approach
because of the symmetry between users and items in all neighborhood-based CF
calculations. In our experiments we used both user-based and item-based
approaches for rating estimation
47. 47
CHAPTER 6
MODULE DESCRIPTION
6.1 MODULES
• Posting the opinion
• Recommendation Technique
• Rating Prediction
• Ranking Approach
6.2 MODULES DESCRIPTION
POSTING THE OPINION:
In this module, we get the opinions from various people about business,
e-commerce and products through online. The opinions may be of two types.
Direct opinion and comparative opinion. Direct opinion is to post a comment about
the components and attributes of products directly. Comparative opinion is to post
a comment based on comparison of two or more products. The comments may be
positive or negative.
RECOMMENDATION TECHNIQUE:
The quality of recommendations can be evaluated along a number of
dimensions, and relying on the accuracy of recommendations alone may not be
enough to find the most relevant items for each User, these studies argue that one
of the goals of recommender systems is to provide a user with highly personalized
items, and more diverse recommendations result in more opportunities for users to
get recommended such items. With this motivation, some studies proposed new
48. 48
recommendation methods that can increase the diversity of recommendation sets
for a given individual user. They can give the feedback of such items.
RATING PREDICTION:
First, the ratings of unrated items are estimated based on the
available information (typically using known user ratings and possibly also
information about item content) using some recommendation algorithm. Heuristic
techniques typically calculate recommendations based directly on the previous user
activities (e.g., transactional data or rating values). For each user, ranks all the
predicted items according to the predicted rating value ranking the candidate
(highly predicted) items based on their predicted rating value, from lowest to
highest (as a result choosing less popular items).
RANKING APPROACH:
Ranking items according to the rating variance of neighbors of a
particular user for a particular item. There exist a number of different ranking
approaches that can improve recommendation diversity by recommending items
other than the ones with topmost predicted rating values to a user. A
comprehensive set of experiments was performed using every rating prediction
technique in conjunction with every recommendation ranking function on every
dataset for different number of top-N recommendations.
49. 49
CHAPTER 7
CONCLUSION
In this paper, we describe the motivation and evolution of WSMA, and it is
analyzed that the existing approaches mainly focus on narrow domains. Aiming at
a more general approach, we propose a novel scheme to answer questions using
media data by leveraging textual answers in cQA. For a given QA pair, our scheme
first predicts which type of medium is appropriate for enriching the original textual
answer. Following that, it automatically generates a query based on the QA
knowledge and then performs multimedia search with the query. Finally, query-
adaptive reranking and duplicate removal are performed to obtain a set of images
and videos for presentation along with the original textual answer. Different from
the conventional WSMA research that aims to automatically generate multimedia
answers with given questions, our approach is built based on the community-
contributed answers, and it can thus deal with more general questions and achieve
better performance
50. 50
APPENDICES-1
Source Code:
Admin Login:
using System;
using System.Data;
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
public partial class adminlogin : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(System.Configuration.ConfigurationManager.AppSettings["con"]);
protected void Button1_Click(object sender, EventArgs e)
{
SqlCommand cmd=new SqlCommand ("select count(*) from login where
uid='"+TextBox1 .Text +"' and pwd='"+ TextBox2 .Text +"' ", con);
con.Open();
51. 51
int c = Convert .ToInt32 (cmd.ExecuteScalar());
if (c > 0)
{
Label3.Text = "successful";
}
else
Label3.Text = "Invalid Data";
con.Close();
}
}
Ask Question:
using System;
using System.Data;
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
using System.Windows.Forms;
52. 52
public partial class Askquest : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(System.Configuration.ConfigurationManager.AppSettings["con"]);
protected void Page_Load(object sender, EventArgs e)
{
DateTime d = Convert.ToDateTime(DateTime.Now.ToLongTimeString());
Label3.Text = d.ToString();
Label6.Text = Session["uid"].ToString();
}
protected void Button1_Click(object sender, EventArgs e)
{
string str = "insert into askqust (ques,qdetail,mailfrom,today)values ('" +
TextBox5.Text + "','" + TextBox1.Text + "','" + Label6.Text + "','" + Label3.Text +
"')";
Connection.ExecuteQuery(str);
MessageBox.Show("Datas Saved");
TextBox1.Text = "";
TextBox5.Text = "";
}
}
Doc Details:
using System;
using System.Data;
53. 53
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
using System.IO;
using System.Windows.Forms;
public partial class docdetails : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(System.Configuration.ConfigurationManager.AppSettings["con"]);
protected void Page_Load(object sender, EventArgs e)
{
string qry = string.Empty;
qry="select *from askqust where qid='"+Request.QueryString["id"]+"'";
DataSet ds=new DataSet();
ds=Connection.ExecuteQuery(qry);
if (ds != null)
{
Label7.Text=ds.Tables[0].Rows[0]["ques"].ToString();
TextBox1.Text=ds.Tables[0].Rows[0]["qdetail"].ToString();
54. 54
}
}
protected void Button1_Click(object sender, EventArgs e)
{
string vfilename = string.Empty;
string filename = string.Empty;
MessageBox.Show("Saved");
}
}
Doc Register:
using System;
using System.Data;
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
public partial class docregister : System.Web.UI.Page
{
56. 56
catch (Exception ex)
{
Response.Redirect(ex.Message);
}
finally
{
if (con.State == ConnectionState.Open)
con.Close();
}
}
}
Register:
using System;
using System.Data;
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
58. 58
con.Close();
}
}
}
Search:
using System;
using System.Collections;
using System.Configuration;
using System.Data;
using System.Linq;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.HtmlControls;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Xml.Linq;
using System.Data.SqlClient;
public partial class search : System.Web.UI.Page
{
DataTable dt = new DataTable();
protected void Button1_Click(object sender, EventArgs e)
{
59. 59
SqlConnection connection = new
SqlConnection(ConfigurationManager.ConnectionStrings["con"].ConnectionString
);
SqlCommand command = new SqlCommand("SELECT * from replydata where
ques like '%"+TextBox1.Text+"%' ", connection);
SqlDataAdapter ada = new SqlDataAdapter(command);
ada.Fill(dt);
gvImages.DataSource = dt;
gvImages.DataBind();
BindGrid();
textbind();
}
private void BindGrid()
{
string strConnString =
ConfigurationManager.ConnectionStrings["con"].ConnectionString;
using (SqlConnection con = new SqlConnection(strConnString))
{
using (SqlCommand cmd = new SqlCommand())
{
cmd.CommandText = "select qid, Name from replydata where ques like'%" +
TextBox1.Text + "%'";
cmd.Connection = con;
con.Open();
DataList1.DataSource = cmd.ExecuteReader();
DataList1.DataBind();
60. 60
con.Close();
}
}
}
void textbind()
{
SqlConnection connection = new
SqlConnection(ConfigurationManager.ConnectionStrings["con"].ConnectionString
);
SqlCommand command = new SqlCommand("SELECT* from replydata where
ques like '%" + TextBox1.Text + "%'", connection);
SqlDataAdapter ada = new SqlDataAdapter(command);
ada.Fill(dt);
GridView1.DataSource = dt;
GridView1.DataBind();
}
}
View:
using System;
using System.Data;
using System.Configuration;
using System.Collections;
using System.Web;
using System.Web.Security;
using System.Web.UI;
61. 61
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Web.UI.HtmlControls;
using System.Data.SqlClient;
public partial class viewdoct : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(System.Configuration.ConfigurationManager.AppSettings["con"]);
protected void Page_Load(object sender, EventArgs e)
{
DataSet ds = new DataSet();
ds = Connection.ExecuteQuery("select * from askqust");
GridView1.DataSource = ds.Tables[0];
GridView1.DataBind();
}
protected void GridView1_RowDeleting(object sender, GridViewDeleteEventArgs
e)
{
Label txtcity = (Label)GridView1.Rows[e.RowIndex].FindControl("Label3");
string m = txtcity.ToString();
Response.Redirect("docdetails.aspx?id=" + txtcity.Text.ToString());
}
72. 72
REFERENCES
[1] L. A. Adamic, J. Zhang, E. Bakshy, andM. S. Ackerman,(2008) “Knowledge
sharing and Yahoo answers: Everyone knows something,” in Proc. Int.
World Wide Web Conf.
[2] J. Cao, F. Jay, and J. Nunamaker,(2004)“Question answering on lecture
videos: A multifaceted approach,” in Proc. Int. Joint Conf. Digital Libraries.
[3] H. Cui, M.-Y. Kan, and T.-S. Chua,(2007) “Soft pattern matching models
for definitional question answering,” ACM Trans. Inf. Syst., vol. 25, no. 2,
pp. 30–30.
[4] L. Nie, M. Wang, Z. Zha, G. Li, and T.-S. Chua,(2011)“Multimedia
answering: Enriching text QA with media information,” in Proc. ACM Int.
SIGIR Conf.
[5] S. A. Quarteroni and S. Manandhar,(2008)“Designing an interactive open
domain question answering system,” J. Natural Lang. Eng., vol. 15, no. 1,
pp. 73–95.
[6] M. Wang, X. S. Hua, T. Mei, R. Hong, G. J. Qi, Y. Song, and L. R.
Dai,(2009) “Semi-supervised kernel density estimation for video annotation,”
Comput. Vision Image Understand., vol. 113, no. 3, pp. 384–396.
[7] R. C. Wang, N. Schlaefer, W. W. Cohen, and E. Nyberg,(2008) “Automatic
set expansion for list question answering,” in Proc. Int. Conf. Empirical
Methods in Natural Language Processing.
[8] Z.-J. Zha, X.-S. Hua, T. Mei, J. Wang, G.-J. Qi, and Z. Wang,(2008)“Joint
multi- label multi-instance learning for image classification,” in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–8.