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Articles 121 - 150 of 175
Full-Text Articles in Data Storage Systems
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Research Collection School Of Computing and Information Systems
With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, …
Efficient Extraction Of Json Information In Sas Using The Scanover Function, Murphy Choy, Kyong Jin Shim
Efficient Extraction Of Json Information In Sas Using The Scanover Function, Murphy Choy, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
JSON, otherwise known as JavaScript Object Notation, is a popular data interchange format which provides a humanreadable format. It is language independent and can be read easily in a variety of computer languages. With the riseof Twitter and other unstructured data, there has been a move to incorporate such data as a way of disseminatinginformation. Twitter currently provides a simple API for users to extract tweets using JSON format. While SAS doesnot currently have a direct way of reading JSON, the SCANOVER function in SAS data step provides user with asimple and effective approach to getting JSON information into SAS …
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Predicting Sql Injection And Cross Site Scripting Vulnerabilities Through Mining Input Sanitization Patterns, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed. Alternatively, there are also vulnerability prediction approaches based on machine learning techniques, which showed that static code attributes such as code complexity measures are cheap and useful predictors. However, current prediction approaches target general vulnerabilities. And most of these approaches locate vulnerable code only at software component or file levels. Some approaches also involve process attributes that …
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Circular Reranking For Visual Search, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among …
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Searching Visual Instances With Topology Checking And Context Modeling, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Instance Search (INS) is a realistic problem initiated by TRECVID, which is to retrieve all occurrences of the querying object, location, or person from a large video collection. It is a fundamental problem with many applications, and also a challenging problem different from the traditional concept or near-duplicate (ND) search, since the relevancy is defined at instance level. True responses could exhibit various visual variations, such as being small on the image with different background, or showing a non-homography spatial configuration. Based on the Bag-of-Words model, we propose two techniques tailored for Instance Search. Specifically, we explore the use of …
Semi-Supervised Heterogeneous Fusion For Multimedia Data Co-Clustering, Lei Meng, Ah-Hwee Tan, Dong Xu
Semi-Supervised Heterogeneous Fusion For Multimedia Data Co-Clustering, Lei Meng, Ah-Hwee Tan, Dong Xu
Research Collection School Of Computing and Information Systems
Co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multi-modal features. In this paper, we propose a generalized form of Heterogeneous Fusion Adaptive Resonance Theory, called GHF-ART, for co-clustering of large-scale web multimedia documents. By extending the two-channel Heterogeneous Fusion ART (HF-ART) to multiple channels, GHF-ART is designed to handle multimedia data with an arbitrarily rich …
Community As A Connector: Associating Faces With Celebrity Names In Web Videos, Zhineng Chen, Chong-Wah Ngo, Juan Cao, Wei Zhang
Community As A Connector: Associating Faces With Celebrity Names In Web Videos, Zhineng Chen, Chong-Wah Ngo, Juan Cao, Wei Zhang
Research Collection School Of Computing and Information Systems
Associating celebrity faces appearing in videos with their names is of increasingly importance with the popularity of both celebrity videos and related queries. However, the problem is not yet seriously studied in Web video domain. This paper proposes a Community connected Celebrity Name-Face Association approach (CCNFA), where the community is regarded as an intermediate connector to facilitate the association. Specifically, with the names and faces extracted from Web videos, C-CNFA decomposes the association task into a three-step framework: community discovering, community matching and celebrity face tagging. To achieve the goal of efficient name-face association under this umbrella, algorithms such as …
Predicting Domain Adaptivity: Redo Or Recycle?, Ting Yao, Chong-Wah Ngo, Shiai Zhu
Predicting Domain Adaptivity: Redo Or Recycle?, Ting Yao, Chong-Wah Ngo, Shiai Zhu
Research Collection School Of Computing and Information Systems
Over the years, the academic researchers have contributed various visual concept classifiers. Nevertheless, given a new dataset, most researchers still prefer to develop large number of classifiers from scratch despite expensive labeling efforts and limited computing resources. A valid question is why not multimedia community “embrace the green” and recycle off-the-shelf classifiers for new dataset. The difficulty originates from the domain gap that there are many different factors that govern the development of a classifier and eventually drive its performance to emphasize certain aspects of dataset. Reapplying a classifier to an unseen dataset may end up GIGO (garbage in, garbage …
Fashionask: Pushing Community Answers To Your Fingertips, Wei Zhang, Lei Pang, Chong-Wah Ngo
Fashionask: Pushing Community Answers To Your Fingertips, Wei Zhang, Lei Pang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
We demonstrate a multimedia-based question-answering system, named FashionAsk, by allowing users to ask questions referring to pictures snapped by mobile devices. Specifically, instead of asking verbose questions to depict visual instances, direct pictures are provided as part of questions. To answer these multi-modal questions, FashionAsk performs a large-scale instance search to infer the names of instances, and then matches with similar questions from communitycontributed QA websites as answers. The demonstration is conducted on a million-scale dataset of Web images and QA pairs in the domain of fashion products. Asking a multimedia question through FashionAsk can take as short as five …
Tracking Web Video Topics: Discovery, Visualization, And Monitoring, Juan Cao, Chong-Wah Ngo, Yong-Dong Zhang, Jin-Tao Li
Tracking Web Video Topics: Discovery, Visualization, And Monitoring, Juan Cao, Chong-Wah Ngo, Yong-Dong Zhang, Jin-Tao Li
Research Collection School Of Computing and Information Systems
Despite the massive growth of web-shared videos in Internet, efficient organization and monitoring of videos remains a practical challenge. While nowadays broadcasting channels are keen to monitor online events, identifying topics of interest from huge volume of user uploaded videos and giving recommendation to emerging topics are by no means easy. Specifically, such process involves discovering of new topic, visualization of the topic content, and incremental monitoring of topic evolution. This paper studies the problem from three aspects. First, given a large set of videos collected over months, an efficient algorithm based on salient trajectory extraction on a topic evolution …
Galaxy Browser: Exploratory Search Of Web Videos, Lei Pang, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Galaxy Browser: Exploratory Search Of Web Videos, Lei Pang, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Most search engines return a ranked list of items in response to a query. The list however tells very little about the relationship among items. For videos especially, users often read to spend significant amount of time to navigate the search result. Exploratory search presents a new paradigm for browsing where the browser takes up the role of information exploring and presents a well-organized browsing structure for users to navigate. The proposed interface Galaxy Browser adopts the recent advances in near-duplicate detection and then synchronizes the detected near-duplicate information with comprehensive background knowledge derived from online external resources. The result …
Cross Media Hyperlinking For Search Topic Browsing, Song Tan, Chong-Wah Ngo, Hung-Khoon Tan, Lei Pang
Cross Media Hyperlinking For Search Topic Browsing, Song Tan, Chong-Wah Ngo, Hung-Khoon Tan, Lei Pang
Research Collection School Of Computing and Information Systems
With the rapid growth of social media, there are plenty of information sources freely available online for use. Nevertheless, how to synchronize and leverage these diverse forms of information for multimedia applications remains a problem yet to be seriously studied. This paper investigates the synchronization of multiple media content in the physical form of hyperlinking them. The ultimate goal is to develop browsing systems that author search results with rich media information mined from various knowledge sources. The authoring enables the vivid visualization and exploration of different information landscapes inherent in search results. Several key techniques are studied in this …
On The Pooling Of Positive Examples With Ontology For Visual Concept Learning, Shiai Zhu, Chong-Wah Ngo, Yu-Gang Jiang
On The Pooling Of Positive Examples With Ontology For Visual Concept Learning, Shiai Zhu, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
A common obstacle in effective learning of visual concept classifiers is the scarcity of positive training examples due to expensive labeling cost. This paper explores the sampling of weakly tagged web images for concept learning without human assistance. In particular, ontology knowledge is incorporated for semantic pooling of positive examples from ontologically neighboring concepts. This effectively widens the coverage of the positive samples with visually more diversified content, which is important for learning a good concept classifier. We experiment with two learning strategies: aggregate and incremental. The former strategy re-trains a new classifier by combining existing and newly collected examples, …
Fusing Heterogeneous Modalities For Video And Image Re-Ranking, Hung-Khoon Tan, Chong-Wah Ngo
Fusing Heterogeneous Modalities For Video And Image Re-Ranking, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Multimedia documents in popular image and video sharing websites such as Flickr and Youtube are heterogeneous documents with diverse ways of representations and rich user-supplied information. In this paper, we investigate how the agreement among heterogeneous modalities can be exploited to guide data fusion. The problem of fusion is cast as the simultaneous mining of agreement from different modalities and adaptation of fusion weights to construct a fused graph from these modalities. An iterative framework based on agreement-fusion optimization is thus proposed. We plug in two well-known algorithms: random walk and semi-supervised learning to this framework to illustrate the idea …
Mining Event Structures From Web Videos, Xiao Wu, Yi-Jie Lu, Qiang Peng, Chong-Wah Ngo
Mining Event Structures From Web Videos, Xiao Wu, Yi-Jie Lu, Qiang Peng, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The article is discussing the issues of mining event structures from Web video search results using text analysis, burst detection, and clustering as with the proliferation of social media, the volume of Web videos have grown exponentially.
Concept-Driven Multi-Modality Fusion For Video Search, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Concept-Driven Multi-Modality Fusion For Video Search, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
As it is true for human perception that we gather information from different sources in natural and multi-modality forms, learning from multi-modalities has become an effective scheme for various information retrieval problems. In this paper, we propose a novel multi-modality fusion approach for video search, where the search modalities are derived from a diverse set of knowledge sources, such as text transcript from speech recognition, low-level visual features from video frames, and high-level semantic visual concepts from supervised learning. Since the effectiveness of each search modality greatly depends on specific user queries, prompt determination of the importance of a modality …
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search engines are used to return a long list of hundreds or even thousands of videos in response to a query topic. Efficient navigation of videos becomes difficult and users often need to painstakingly explore the search list for a gist of the search result. This paper addresses the challenge of topical summarization by providing a timeline-based visualization of videos through matching of heterogeneous sources. To overcome the so called sparse-text problem of web videos, auxiliary information from Google context is exploited. Google Trends is used to predict the milestone events of a topic. Meanwhile, the typical scenes of web …
Data-Driven Approaches To Community-Contributed Video Applications, Xiao Wu, Chong-Wah Ngo, Wan-Lei Zhao
Data-Driven Approaches To Community-Contributed Video Applications, Xiao Wu, Chong-Wah Ngo, Wan-Lei Zhao
Research Collection School Of Computing and Information Systems
With the prosperity of video-sharing websites such as YouTube, the amount of community-contributed video has increased dramatically. Reportedly more than 65,000 new videos were uploaded to YouTube every day in July 2006 and it's estimated that 20 hours of new videos were uploaded to the site every minute in May 2009. In addition to the huge volume of video data, the social Web provides rich contextual and social resources associated with videos. These resources include title, tags, thumbnails, related videos, and user and community information, as illustrated in Figure 1. While billions of user-generated videos accompanied with rich-media information have …
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
We introduce MobiTOP, a Web-based system for organizing and retrieving hierarchical location-based annotations. Each annotation contains multimedia content (such as text, images, video) associated with a location, and users are able to annotate existing annotations to an arbitrary depth, in effect creating a hierarchy. An evaluation was conducted on a group of potential users to ascertain their perceptions of the usability of the application. The results were generally positive and the majority of the participants saw MobiTOP as a useful platform to share location-based information. We conclude with implications of our work and opportunities for future research.
On The Annotation Of Web Videos By Efficient Near-Duplicate Search, Wan-Lei Zhao, Xiao Wu, Chong-Wah Ngo
On The Annotation Of Web Videos By Efficient Near-Duplicate Search, Wan-Lei Zhao, Xiao Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
With the proliferation of Web 2.0 applications, usersupplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues …
On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang
On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Visual concept learning often requires a large set of training images. In practice, nevertheless, acquiring noise-free training labels with sufficient positive examples is always expensive. A plausible solution for training data collection is by sampling the largely available user-tagged images from social media websites. With the general belief that the probability of correct tagging is higher than that of incorrect tagging, such a solution often sounds feasible, though is not without challenges. First, user-tags can be subjective and, to certain extent, are ambiguous. For instance, an image tagged with “whales” may be simply a picture about ocean museum. Learning concept …
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Research Collection School Of Computing and Information Systems
Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive singlebased modeling audio retrieval approaches, BoA is a highlevel model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient …
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Most existing reranking approaches to image search focus solely on mining “visual” cues within the initial search results. However, the visual information cannot always provide enough guidance to the reranking process. For example, different images with similar appearance may not always present the same relevant information to the query. Observing that multi-modality cues carry complementary relevant information, we propose the idea of co-reranking for image search, by jointly exploring the visual and textual information. Co-reranking couples two random walks, while reinforcing the mutual exchange and propagation of information relevancy across different modalities. The mutual reinforcement is iteratively updated to constrain …
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper presents an innovative style-wise advertising platform for web page. Web page “style” mainly refers to visual effects, such as color and layout. Unlike the most popular ad-network such as Google AdSense which needs publishers to change the original structure of their pages and define the position and style of the embedded ads manually, stylewise page advertising aims to automatically deliver styleconsistent ads at proper positions within the web page, without breaking the layout of the original page. Our system is motivated from the fact that almost 90% web pages contain blank regions without any content. Given a web …
A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li
A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li
Research Collection School Of Computing and Information Systems
An intrusion tolerant database uses damage assessment techniques to detect damage propagation scales in a corrupted database system. Traditional damage assessment approaches in a intrusion tolerant database system can only locate damages which are caused by reading corrupted data. In fact, there are many other damage spreading patterns that have not been considered in traditional damage assessment model. In this paper, we systematically analyze inter-transaction dependency relationships that have been neglected in the previous research and propose four different dependency relationships between transactions which may cause damage propagation. We extend existing damage assessment model based on the four novel dependency …
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual …
Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Research Collection School Of Computing and Information Systems
The Bag-of-Words (BoW) model is a promising image representation for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between …
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Following the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely …
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Web video categorization is a fundamental task for web video search. In this paper, we explore the Google challenge from a new perspective by combing contextual and social information under the scenario of social web. The semantic meaning of text (title and tags), video relevance from related videos, and user interest induced from user videos, are integrated to robustly determine the video category. Experiments on YouTube videos demonstrate the effectiveness of the proposed solution. The performance reaches 60% improvement compared to the traditional text based classifiers.
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Research Collection School Of Computing and Information Systems
Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided …