Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (6876)
- Computer Sciences (6868)
- Databases and Information Systems (2875)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1253)
-
- Engineering (957)
- Numerical Analysis and Scientific Computing (844)
- Information Security (709)
- Graphics and Human Computer Interfaces (627)
- Social and Behavioral Sciences (570)
- Computer Engineering (512)
- Business (461)
- Operations Research, Systems Engineering and Industrial Engineering (418)
- Theory and Algorithms (387)
- Programming Languages and Compilers (333)
- OS and Networks (259)
- Communication (239)
- Social Media (194)
- Public Affairs, Public Policy and Public Administration (186)
- Data Storage Systems (176)
- Transportation (170)
- Education (152)
- Medicine and Health Sciences (140)
- Digital Communications and Networking (123)
- Management Information Systems (98)
- Computer and Systems Architecture (89)
- International and Area Studies (89)
- Asian Studies (88)
- Health Information Technology (83)
- Technology and Innovation (78)
- Keyword
-
- Deep learning (112)
- Machine learning (107)
- Social media (63)
- Data mining (59)
- Reinforcement learning (58)
-
- Artificial intelligence (55)
- Empirical study (55)
- Deep Learning (49)
- Software engineering (49)
- Optimization (47)
- Privacy (47)
- Task analysis (46)
- Cloud computing (45)
- Online learning (45)
- Visualization (45)
- Neural networks (44)
- Security (43)
- Training (43)
- Feature extraction (42)
- Anomaly detection (41)
- Collaboration (41)
- Singapore (41)
- Twitter (41)
- Large Language Models (36)
- Semantics (36)
- Android (35)
- Natural language processing (35)
- Classification (34)
- Machine Learning (33)
- Blockchain (32)
- Publication Year
- File Type
Articles 6061 - 6090 of 7017
Full-Text Articles in Entire DC Network
Modeling Video Hyperlinks With Hypergraph For Web Video Reranking, Hung-Khoon Tan, Chong-Wah Ngo, Xiao Wu
Modeling Video Hyperlinks With Hypergraph For Web Video Reranking, Hung-Khoon Tan, Chong-Wah Ngo, Xiao Wu
Research Collection School Of Computing and Information Systems
In this paper, we investigate a novel approach of exploiting visual-duplicates for web video reranking using hypergraph. Current graph-based reranking approaches consider mainly the pair-wise linking of keyframes and ignore reliability issues that are inherent in such representation. We exploit higher order relation to overcome the issues of missing links in visual-duplicate keyframes and in addition identify the latent relationships among keyframes. Based on hypergraph, we consider two groups of video threads: visual near-duplicate threads and story threads, to hyperlink web videos and describe the higher order information existing in video content. To facilitate reranking using random walk algorithm, the …
Remote Attestation On Program Execution, Liang Gu, Xuhua Ding, Robert H. Deng, Bing Xie, Hong Mei
Remote Attestation On Program Execution, Liang Gu, Xuhua Ding, Robert H. Deng, Bing Xie, Hong Mei
Research Collection School Of Computing and Information Systems
Remote attestation provides the basis for one platform to establish trusts on another. In this paper, we consider the problem of attesting the correctness of program executions. We propose to measure the target program and all the objects it depends on, with an assumption that the Secure Kernel and the Trusted Platform Module provide a secure execution environment through process separation. The attestation of the target program begins with a program analysis on the source code or the binary code in order to find out the relevant executables and data objects. Whenever such a data object is accessed or a …
Event Detection With Common User Interests, Meishan Hu, Aixin Sun, Ee Peng Lim
Event Detection With Common User Interests, Meishan Hu, Aixin Sun, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In this paper, we aim at detecting events of common user interests from huge volume of user-generated content. The degree of interest from common users in an event is evidenced by a significant surge of event-related queries issued to search for documents (e.g., news articles, blog posts) relevant to the event. Taking the stream of queries from users and the stream of documents as input, our proposed framework seamlessly integrates the two streams into a single stream of query profiles. A query profile is a set of documents matching a query at a given time. With the single stream of …
Specification Mining Of Symbolic Scenario-Based Models, David Lo, Shahar Maoz
Specification Mining Of Symbolic Scenario-Based Models, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Many dynamic analysis approaches to specification mining, which extract behavioral models from execution traces, do not consider object identities. This limits their power when used to analyze traces of general object oriented programs. In this work we present a novel specification mining approach that considers object identities, and, moreover, generalizes from specifications involving concrete objects to their symbolic class-level abstractions. Our approach uses data mining methods to extract significant scenario-based specifications in the form of Damm and Harel's live sequence charts (LSC), a formal and expressive extension of classic sequence diagrams. We guarantee that all mined symbolic LSCs are significant …
Resource-Aware Video Multicasting Via Access Gateways In Wireless Mesh Networks, Wanqing Tu, Cormac J. Sreenan, Chun Tung Chou, Archan Misra
Resource-Aware Video Multicasting Via Access Gateways In Wireless Mesh Networks, Wanqing Tu, Cormac J. Sreenan, Chun Tung Chou, Archan Misra
Research Collection School Of Computing and Information Systems
This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy multi-hop wireless paths with low capacity. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the alternative channel assignment algorithm. These algorithms use different approaches to arrange multicast group members into a clustered and two-tier integrated architecture in which network protocols can make use of …
Near-Duplicate Keyframe Retrieval By Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan
Near-Duplicate Keyframe Retrieval By Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Near-duplicate image retrieval plays an important role in many real-world multimedia applications. Most previous approaches have some limitations. For example, conventional appearance-based methods may suffer from the illumination variations and occlusion issue, and local feature correspondence-based methods often do not consider local deformations and the spatial coherence between two point sets. In this paper, we propose a novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora. In contrast to previous approaches, the NIM technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and …
Representative Entry Selection For Profiling Blogs, Jinfeng Zhuang, Steven C. H. Hoi, Aixin Sun, Rong Jin
Representative Entry Selection For Profiling Blogs, Jinfeng Zhuang, Steven C. H. Hoi, Aixin Sun, Rong Jin
Research Collection School Of Computing and Information Systems
Many applications on blog search and mining often meet the challenge of handling huge volume of blog data, in which one single blog could contain hundreds or even thousands of entries. We investigate novel techniques for profiling blogs by selecting a subset of representative entries for each blog. We propose two principles for guiding the entry selection task: representativeness and diversity. Further, we formulate the entry selection task into a combinatorial optimization problem and propose a greedy yet effective algorithm for finding a good approximate solution by exploiting the theory of submodular functions. We suggest blog classification for judging the …
Output Regularized Metric Learning With Side Information, Wei Liu, Steven C. H. Hoi, Jianzhuang Liu
Output Regularized Metric Learning With Side Information, Wei Liu, Steven C. H. Hoi, Jianzhuang Liu
Research Collection School Of Computing and Information Systems
Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgements between image pairs. Exploiting the side information as well as inherent neighborhood structures among examples, we design a convex regularizer upon which a novel distance metric learning approach, named output regularized metric learning, is presented to tackle collaborative image retrieval. Different from previous distance metric …
An Effective Approach To 3d Deformable Surface Tracking, Jianke Zhu, Steven C. H. Hoi, Zenglin Xu, Michael R. Lyu
An Effective Approach To 3d Deformable Surface Tracking, Jianke Zhu, Steven C. H. Hoi, Zenglin Xu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent state-of-the-art approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility problem. The main drawback of this solution is the high computational cost. In this paper, we first reformulate the problem into an unconstrained quadratic optimization problem. Instead of handling a large set of complicated SOCP constraints, our new formulation can be solved very efficiently by resolving a set of sparse linear equations. Based on the new framework, a …
Tuning Into The Digital Channel: Evaluating Business Model Fit For Internet Firm Survival, Robert J. Kauffman, Bin Wang
Tuning Into The Digital Channel: Evaluating Business Model Fit For Internet Firm Survival, Robert J. Kauffman, Bin Wang
Research Collection School Of Computing and Information Systems
More than 5,000 Internet firms have failed since the beginning of 2000. One common perception is that the downturn in the economy drove many firms out of business. But then, why have some firms survived? In this research, we provide an empirical analysis by examining how the business model characteristics of an Internet firm affect its survival. We analyze a panel data set of 130 public Internet firms using two different techniques: non-parametric survival analysis, and the semiparametric Cox proportional hazards model. We characterize the survival rates throughout the lifetimes of the public Internet firms in our sample. Our results …
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
We consider the Resource-Constrained Project Scheduling Problem with minimal and maximal time lags under resource and duration uncertainties. To manage resource uncertainties, we build upon the work of Lambrechts et al 2007 and develop a method to analyze the effect of resource breakdowns on activity durations. We then extend the robust local search framework of Lau et al 2007 with additional considerations on the impact of unexpected resource breakdowns to the project makespan, so that partial order schedules (POS) can absorb both resource and duration uncertainties. Experiments show that our proposed model is capable of addressing the uncertainty of resources, …
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
Research Collection School Of Computing and Information Systems
Through empirical comparison of classical job shop problems (JSP) with multi-machine consideration, we find that the objective to minimize the sum of weighted tardiness has a better wait property compared with the objective to minimize the makespan. Further, we test the proposed Iterative Minimization Micro-model (IMM) heuristic method with the mixed integer programming (MIP) solution by CPLEX. For multi-machine problems, the IMM heuristic method is faster and achieves a better solution. Finally, for a large problem instance with 409 jobs and 30 types of machines, IMM-heuristic method is compared with ProModel and we find that the heuristic method is slightly …
Tagnsearch: Searching And Navigating Geo-Referenced Collections Of Photographs, Quang Minh Nguyen, Thi Nhu Quynh Kim, Dion Hoe-Lian Goh, Yin-Leng Theng, Ee Peng Lim, Aixin Sun, Chew-Hung Chang, Kalyani Chatterjea
Tagnsearch: Searching And Navigating Geo-Referenced Collections Of Photographs, Quang Minh Nguyen, Thi Nhu Quynh Kim, Dion Hoe-Lian Goh, Yin-Leng Theng, Ee Peng Lim, Aixin Sun, Chew-Hung Chang, Kalyani Chatterjea
Research Collection School Of Computing and Information Systems
TagNSearch is a map-based tool for searching and browsing geo-tagged photographs based on their associated tags. Using Flickr as the dataset, TagNSearch returns, for a given query, photographs clustered by locations, and summarizes each cluster of photographs by cluster-specific tags. A map-based interface is also provided to help users better search, navigate and browse photographs and their clusters. A qualitative evaluation comparing TagNSearch and an existing tag search support in Flickr was also conducted. The task involved finding locations associated with a set of photographs. Participants were found to perform this task better using TagNSearch than Flickr.
An Efficient Pir Construction Using Trusted Hardware, Yanjiang Yang, Xuhua Ding, Robert H. Deng, Feng Bao
An Efficient Pir Construction Using Trusted Hardware, Yanjiang Yang, Xuhua Ding, Robert H. Deng, Feng Bao
Research Collection School Of Computing and Information Systems
For a private information retrieval (PIR) scheme to be deployed in practice, low communication complexity and low computation complexity are two fundamental requirements it must meet. Most existing PIR schemes only focus on the communication complexity. The reduction on the computational complexity did not receive the due treatment mainly because of its O(n) lower bound. By using the trusted hardware based model, we design a novel scheme which breaks this barrier. With constant storage, the computation complexity of our scheme, including offline computation, is linear to the number of queries and is bounded by after optimization.
Software Nightmares, Manoj Thulasidas
Software Nightmares, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
To err is human, but to really foul things up, you need a computer. So states the remarkably insightful Murphy’s Law. And nowhere else does this ring truer than in our financial workplace. After all, it is the financial sector that drove the rapid progress in the computing industry – which is why the first computing giant had the word “business” in its name. The financial industry keeps up with the developments in the computer industry for one simple reason. Stronger computers and smarter programs mean more money — a concept we readily grasp. As we use the latest and …
Relative Importance, Specific Investment And Ownership In Interorganizational Systems., Kunsoo Han, Robert J. Kauffman, Barrie R. Nault
Relative Importance, Specific Investment And Ownership In Interorganizational Systems., Kunsoo Han, Robert J. Kauffman, Barrie R. Nault
Research Collection School Of Computing and Information Systems
Implementation and maintenance of interorganizational systems (IOS) require investments by all the participating firms. Compared with intraorganizational systems, however, there are additional uncertainties and risks. This is because the benefits of IOS investment depend not only on a firm's own decisions, but also on those of its business partners. Without appropriate levels of investment by all the firms participating in an IOS, they cannot reap the full benefits. Drawing upon the literature in institutional economics, we examine IOS ownership as a means to induce value-maximizing noncontractible investments. We model the impact of two factors derived from the theory of incomplete …
Simulating A Smartboard By Real-Time Gesture Detection In Lecture Videos, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Simulating A Smartboard By Real-Time Gesture Detection In Lecture Videos, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Research Collection School Of Computing and Information Systems
Gesture plays an important role for recognizing lecture activities in video content analysis. In this paper, we propose a real-time gesture detection algorithm by integrating cues from visual, speech and electronic slides. In contrast to the conventional "complete gesture" recognition, we emphasize detection by the prediction from "incomplete gesture". Specifically, intentional gestures are predicted by the modified hidden Markov model (HMM) which can recognize incomplete gestures before the whole gesture paths are observed. The multimodal correspondence between speech and gesture is exploited to increase the accuracy and responsiveness of gesture detection. In lecture presentation, this algorithm enables the on-the-fly editing …
Mining Patterns And Rules For Software Specification Discovery, David Lo, Siau-Cheng Khoo
Mining Patterns And Rules For Software Specification Discovery, David Lo, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Software specifications are often lacking, incomplete and outdated in the industry. Lack and incomplete specifications cause various software engineering problems. Studies have shown that program comprehension takes up to 45% of software development costs. One of the root causes of the high cost is the lack-of documented specification. Also, outdated and incomplete specification might potentially cause bugs and compatibility issues. In this paper, we describe novel data mining techniques to mine or reverse engineer these specifications from the pool of software engineering data. A large amount of software data is available for analysis. One form of software data is program …
Critical Success Factors In Soa Implementation, J. Erickson, Keng Siau
Critical Success Factors In Soa Implementation, J. Erickson, Keng Siau
Research Collection School Of Computing and Information Systems
Service Oriented Architecture (SOA) has become flavor du jour for many businesses. Seemingly, almost every company has implemented, is in the midst of implementing or is seriously considering a SOA project. A critical question many organizations are facing now is – what are the critical success factors for SOA implementations? This research aims to identify a list of factors relating to SOA implementation success. A Delphi study forms the research method, and inputs regarding SOA critical success factors are requested from a panel of experts.
Integrating Lightweight Systems Analysis Into The Unified Process By Using Service Responsibility Tables, X. Tan, S. Alter, Keng Siau
Integrating Lightweight Systems Analysis Into The Unified Process By Using Service Responsibility Tables, X. Tan, S. Alter, Keng Siau
Research Collection School Of Computing and Information Systems
This paper is a step toward establishing direct, but non-automatic links between lightweight (semi-formal) analysis methods for business professionals and heavyweight analysis methods for IT professionals. After noting the importance of user involvement in obtaining accurate and meaningful user requirements, the paper summarizes the Unified Process, a software development methodology that employs Unified Modeling Language (UML). Another section in the paper summarizes previous extensions of the work system method that produced a lightweight analysis tool called Service Responsibility Tables (SRTs). This paper uses a straightforward example to demonstrate a set of heuristics for translating between service responsibility tables produced by …
Impacts Of Social Network Structure On Knowledge Sharing In Open Source Software Development Teams, Y. Long, Keng Siau
Impacts Of Social Network Structure On Knowledge Sharing In Open Source Software Development Teams, Y. Long, Keng Siau
Research Collection School Of Computing and Information Systems
The study examines the relationship between social network structure and knowledge sharing in Open Source Software (OSS) development teams. One hundred and fifty projects were selected from SourceForge.net using stratified sampling. Social network structure was measured by two indices: degree of centralization and core/periphery fitness. Knowledge sharing was measured from two aspects: the quality of knowledge sharing that is indicated by the helpfulness of messages and the quantity of knowledge sharing that is indicated by the number of messages. The results show that social network structure significantly affects the quantity of knowledge sharing. However, social network structure does not influence …
Knowledge Transfer Via Multiple Model Local Structure Mapping, Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
Knowledge Transfer Via Multiple Model Local Structure Mapping, Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
Research Collection School Of Computing and Information Systems
The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple …
Authenticating The Query Results Of Text Search Engines, Hwee Hwa Pang, Kyriakos Mouratidis
Authenticating The Query Results Of Text Search Engines, Hwee Hwa Pang, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
The number of successful attacks on the Internet shows that it is very difficult to guarantee the security of online search engines. A breached server that is not detected in time may return incorrect results to the users. To prevent that, we introduce a methodology for generating an integrity proof for each search result. Our solution is targeted at search engines that perform similarity-based document retrieval, and utilize an inverted list implementation (as most search engines do). We formulate the properties that define a correct result, map the task of processing a text search query to adaptations of existing threshold-based …
Hierarchical Inter-Object Traces For Specification Mining, David Lo, Shahar Maoz
Hierarchical Inter-Object Traces For Specification Mining, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Major challenges of dynamic analysis approaches to specification mining include scalability over long traces as well as comprehensibility and expressivity of results. We present a novel use of object hierarchies over inter-object traces as an abstraction/refinement mechanism enabling scalable, incremental, top-down mining of scenario-based specifications.
Learning Outcomes For A Business Information Systems Undergraduate Program, Joelle Elmaleh, Steven Miller, Paul S. Goodman
Learning Outcomes For A Business Information Systems Undergraduate Program, Joelle Elmaleh, Steven Miller, Paul S. Goodman
Research Collection School Of Computing and Information Systems
We present a learning outcomes framework for an Information Systems (IS) undergraduate program. This framework includes a supporting software application that goes beyond any other attempt reported to date to integrate learning outcomes into program-wide and course-wide curriculum development, ongoing curriculum redesign, and student learning support. The Learning Outcomes Management System (LOMS) described here is a first-of-a-kind information system created as part of implementing a program-wide learning outcomes framework in a university setting. Our learning outcomes framework is distinct in that 1) it is based on a three-level hierarchically structured definition of learning outcomes that consistently apply to both the …
A Lightweight Buyer-Seller Watermarking Protocol, Yongdong Wu, Hwee Hwa Pang
A Lightweight Buyer-Seller Watermarking Protocol, Yongdong Wu, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
The buyer-seller watermarking protocol enables a seller to successfully identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Based on finite field theory and the homomorphic property of public key cryptosystems such as RSA, several buyer-seller watermarking protocols (N. Memon and P. W. Wong (2001) and C.-L. Lei et al. (2004)) have been proposed previously. However, those protocols require not only large computational power but also substantial network bandwidth. In this paper, we introduce a new buyer-seller protocol that overcomes those weaknesses by managing the watermarks. Compared with the earlier protocols, ours is …
When ‘Good Good’ Can Be Bad, M. Thulasidas
When ‘Good Good’ Can Be Bad, M. Thulasidas
Research Collection School Of Computing and Information Systems
Should we embrace politeness, as Hollywood would have us practise it?
Classification In P2p Networks By Bagging Cascade Rsvms, Hock Hee Ang, Vikvekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng, Anwitaman Datta
Classification In P2p Networks By Bagging Cascade Rsvms, Hock Hee Ang, Vikvekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng, Anwitaman Datta
Research Collection School Of Computing and Information Systems
Data mining tasks in P2P are bound by issues like scalability, peer dynamism, asynchronism, and data privacy preservation. These challenges pose difficulties for deploying conventional machine learning techniques in P2P networks, which may be hard to achieve classification accuracies comparable to regular centralized solutions. We recently investigated the classification problem in P2P networks and proposed a novel P2P classification approach by cascading Reduced Support Vector Machines (RSVM). Although promising results were obtained, the existing solution has some drawback of redundancy in both communication and computation. In this paper, we present a new approach to over the limitation of the previous …
Bag-Of-Visual-Words Expansion Using Visual Relatedness For Video Indexing, Yu-Gang Jiang, Chong-Wah Ngo
Bag-Of-Visual-Words Expansion Using Visual Relatedness For Video Indexing, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Bag-of-visual-words (BoW) has been popular for visual classification in recent years. In this paper, we propose a novel BoW expansion method to alleviate the effect of visual word correlation problem. We achieve this by diffusing the weights of visual words in BoW based on visual word relatedness, which is rigorously defined within a visual ontology. The proposed method is tested in video indexing experiment on TRECVID-2006 video retrieval benchmark, and an improvement of 7% over the traditional BoW is reported.
A Self-Organizing Neural Model For Multimedia Information Fusion, Luong-Dong Nguyen, Kia-Yan Woon, Ah-Hwee Tan
A Self-Organizing Neural Model For Multimedia Information Fusion, Luong-Dong Nguyen, Kia-Yan Woon, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents a self-organizing network model for the fusion of multimedia information. By synchronizing the encoding of information across multiple media channels, the neural model known as fusion Adaptive Resonance Theory (fusion ART) generates clusters that encode the associative mappings across multimedia information in a real-time and continuous manner. In addition, by incorporating a semantic category channel, fusion ART further enables multimedia information to be fused into predefined themes or semantic categories. We illustrate the fusion ART’s functionalities through experiments on two multimedia data sets in the terrorist domain and show the viability of the proposed approach.