Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level.

Abstract A six‐month‐long Atlantic hurricane season impacts Florida residents every year and can result in devastating consequences, including loss of life, property damage, and business interruptions. Hurricane risk assessment and loss prediction are critical to various uses such as determining homeowner insurance premiums, regulating these premiums, conducting scenario analysis, conducting stress tests for companies, disaster management, and evaluating the benefits of disaster mitigation techniques. This article describes the Florida Public Hurricane Loss Model (FPHLM): a large‐scale catastrophe model with massive databases and analytics tools for business and government decision‐making. We will discuss the design and implementation of each component in FPHLM and explain the tools and techniques utilized to tackle challenges in data availability, data analytics, and the interface between the data, analytical techniques, and computing. Results are shown to validate the software system's effectiveness and reliability and illustrate some of the system's use cases.

Pictures or videos captured from a low-altitude aircraft or an unmanned aerial vehicle are a fast and cost-effective way to survey the affected scene for the quick and precise assessment of a catastrophic event’s impacts and damages. Using advanced techniques, such as deep learning, it is now possible to automate the description of disaster scenes and identify features in captured images or recorded videos to gain situational awareness. However, building a large-scale, high-quality dataset with annotated disaster-related features for supervised model training is time-consuming and costly. In this article, we propose a weakly supervised approach to train a deep neural network on low-altitude imagery with highly imbalanced and noisy crowd-sourced labels. We further make use of the rich spatiotemporal data obtained from the pictures and its sequence information to enhance the model’s performance during training via label propagation. Our approach achieves the highest score among all the submitted runs in the TRECVID2020 Disaster Scene Description and Indexing (DSDI) Challenge, indicating its superior capabilities in retrieving disaster-related video clips compared to other proposed methods.

THE COVID-19 pandemic has brought the global society to a historic turning point. While the current pandemic is transforming our normal lives in an unprecedented way, the computing and information services ecosystem have provided the much-needed support to the continuity of the social interactions and the organizational functions. As COVID-19 continues to transform the global society, it is also exposing the weaknesses of the current services ecosystems and newer research challenges to further leveraging emerging computing and services technologies to provide secure, privacy-aware and resilient infrastructure and services during pandemics. The goal of this special issue has been to solicit impactful research papers that will be of immediate value to the ongoing COVID-19 pandemic as well as for future pandemic and crises.

Nowadays, the world faces extreme climate changes, resulting in an increase of natural disaster events and their severities. In these conditions, the necessity of disaster information management systems has become more imperative. Specifically, in this paper, the problem of flood event detection from images with real-world conditions is addressed. That is, the images may be taken in several conditions, including day, night, blurry, clear, foggy, rainy, different lighting conditions, etc. All these abnormal scenarios significantly reduce the performance of the learning algorithms. In addition, many existing image classification methods use datasets that usually include high-resolution images without considering real-world noise. In this paper, we propose a new image classification framework based on adversarial data augmentation and deep learning algorithms to address the aforementioned problems. We validate the performance of the flood event detection framework on a real-world noisy visual dataset collected from social networks.

Talking and literary reading are important activities for children, especially for children with autism spectrum disorder (ASD). We try to integrate the activities with NAO robots to excite their communication willingness. In this paper, a novel multimodal picture book recommendation framework that combines textual information and image information to calculate the similarity between the picture books and the conversation topics is proposed and evaluated using a testing dataset. In the proposed framework, an image neighbor discovery method to get more relative terms and an near-duplicated keyframes (NDK) friend detection method to get more relative NDKs are proposed. Finally, the booklist generated from the experiment is evaluated by six performance indicators and the experimental results demonstrate that our proposed framework achieves satisfactory and promising performance. With the help of the proposed recommendation framework, an autistic child can talk to the NAO robot in a relaxed and enjoyable environment. Please note that the proposed framework is not evaluated for its performance with the ASD children but for its performance at recommending books based on visual and textual features. Therefore, no tests were performed with either professionals nor diagnosed individuals.

Multimedia concept detection is a challenging topic due to the well-known class imbalance issue, where the data instances are distributed unevenly across different classes. This problem becomes even more prominent when the minority class that contains an extremely small proportion of the data represents the concept of interest as has occurred in many real-world applications such as frauds in banking transactions and goal events in soccer videos. Traditional data mining approaches often have difficulty handling largely skewed data distributions. To address this issue, in this paper, an importance-factor (IF)-based multiple correspondence analysis (MCA) framework is proposed to deal with the imbalanced datasets. Specifically, a hierarchical information gain analysis method, which is inspired by the decision tree algorithm, is presented for critical feature selection and IF assignment. Then, the derived IF is incorporated with the MCA algorithm for effective concept detection and retrieval. The comparison results in video concept detection using the disaster dataset and the soccer dataset demonstrate the effectiveness of the proposed framework.

I am very happy to report that TSC has gained an Impact Factor (IF) of 3.520 and the 5-year IF of 4.245, both of which represent significant increases from the previous years. This further speaks to the global reputation of the journal and the amazing work done by the past EICs, all the current and past EB members, and reviewers - all of whom have volunteered their precious time despite their very busy schedule to support and contribute to the growth of this journal. I hope to count on your continued engagement for the future growth of this journal. Over this past year, several esteemed EB members have completed their terms of service to TSC after serving for several years. On behalf of the Services Computing community and the TSC EAB, I would like to thank the following Associate Editors who retired from TSC EB in 2017 for their invaluable service and contributions to the journal. Overall, I am very proud of the success that TSC has achieved in 2017. This would not have been possible without the continued support of the authors, readers, reviewers, TSC EAB, TSC EB, and the staff of IEEE and IEEE Computer Society. I look forward to exploring ways to further enhance the reputation and impact of our journal. I would love to hear your suggestions and comments, and I hope to have your continued support.

Deep learning has led to many breakthroughs in machine perception and data mining. Although there are many substantial advances of deep learning in the applications of image recognition and natural language processing, very few work has been done in video analysis and semantic event detection. Very deep inception and residual networks have yielded promising results in the 2014 and 2015 ILSVRC challenges, respectively. Now the question is whether these architectures are applicable to and computationally reasonable in a variety of multimedia datasets. To answer this question, an efficient and lightweight deep convolutional network is proposed in this paper. This network is carefully designed to decrease the depth and width of the state-of-the-art networks while maintaining the high-performance. The proposed deep network includes the traditional convolutional architecture in conjunction with residual connections and very light inception modules. Experimental results demonstrate that the proposed network not only accelerates the training procedure, but also improves the performance in different multimedia classification tasks.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. However, the detection of these events is such an important research problem and has attracted significant research efforts as lots of real-world big data sets have skewed class distributions. Despite extensive research efforts, rare class mining remains one of the most challenging problems in information science, especially for multimedia big data. Though inter-concept correlations have been utilized to address this issue recently, the very small number of instances in the minority class often lead to the detection of imprecise correlations and unsatisfactory classification results. This paper proposes a novel concept correlation analysis strategy framework using the correlations between the retrieval scores and labels. By integrating the correlation information, the proposed framework can help imbalance data classification and enhance rare class (or concept) mining even with trivial scores from the minority class. Experimental results on the TRECVID multimedia big benchmark data set demonstrate the effectiveness of the proposed framework with promising performance.

News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate keyframes (NDK) detection. Such characteristics have made the mining of the events from news web videos a challenging task. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Cooccurrence and visual near-duplicate feature trajectory induced from NDKs are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated. Experiments on the news web videos from YouTube demonstrate that the integration of visual temporal information and textual distribution information outperforms the existing methods in the news web video event mining.

Classification of imbalanced data is an important research problem as lots of real-world data sets have skewed class distributions in which the majority of data instances (examples) belong to one class and far fewer instances belong to others. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), a classifier induced from an imbalanced data set is more likely to be biased towards the majority class and show very poor classification accuracy on the minority class. Despite extensive research efforts, imbalanced data classification remains one of the most challenging problems in data mining and machine learning, especially for multimedia data. To tackle this challenge, in this paper, we propose an extended deep learning approach to achieve promising performance in classifying skewed multimedia data sets. Specifically, we investigate the integration of bootstrapping methods and a state-of-the-art deep learning approach, Convolutional Neural Networks (CNNs), with extensive empirical studies. Considering the fact that deep learning approaches such as CNNs are usually computationally expensive, we propose to feed low-level features to CNNs and prove its feasibility in achieving promising performance while saving a lot of training time. The experimental results show the effectiveness of our framework in classifying severely imbalanced data in the TRECVID data set.

The optimal dosing regimen of remifentanil for relieving labor pain should achieve maximal efficacy during contractions and little effect between contractions. Toward such a need, we propose a knowledge-assisted sequential pattern analysis with heuristic parameter tuning to predict the changes in intrauterine pressure,which indicates the occurrence of labor contractions. This enables giving the drug shortly before each contraction starts. Asequential association rule mining based patient selection strategy is designed to dynamically select data for training regression models. A novel heuristic parameter tuning method is proposed to decide the appropriate value ranges and searching strategies for both the regularization factor and the Gaussian kernel parameter of leastsquares support vector machine with radial basis function (RBF) kernel, which is used as the regression model for time series prediction. The parameter tuning method utilizes information extracted from the training dataset, and it is adaptive to the characteristics of time series. The promising experimental results show that the proposed framework is able to achieve the lowest prediction errors as compared to some existing methods.

In current biological image analysis, the temporal stage information, such as the developmental stage in the Drosophila development in situ hybridization images, is important for biological knowledge discovery. Such information is usually gained through visual inspection by experts. However, as the high-throughput imaging technology becomes increasingly popular, the demand for labor effort on annotating, labeling, and organizing the images for efficient image retrieval has increased tremendously, making manual data processing infeasible. In this paper, a novel multi-layer classification framework is proposed to discover the temporal information of the biological images automatically. Rather than solving the problem directly, the proposed framework uses the idea of ``divide and conquer'' to create some middle level classes, which are relatively easy to annotate, and to train the proposed subspace-based classifiers on the subsets of data belonging to these categories. Next, the results from these classifiers are integrated to improve the final classification performance. In order to appropriately integrate the outputs from different classifiers, a multi-class based closed form quadratic cost function is defined as the optimization target and the parameters are estimated using the gradient descent algorithm. Our proposed framework is tested on three biological image data sets and compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed middle-level classes and the proper integration of the results from the corresponding classifiers are promising for mining the temporal stage information of the biological images.

Most content-based recommender systems focus on analyzing the textual information of items. For items with images, the images can be treated as another information modality. In this paper, an effective method called MSLIM is proposed to integrate multimodal information for content-based item recommendation. It formalizes the probelm into a regularized optimization problem in the least-squares sense and the coordinate gradient descent is applied to solve the problem. The aggregation coefficients of the items are learned in an unsupervised manner during this process, based on which the k-nearest neighbor (k-NN) algorithm is used to generate the top-N recommendations of each item by finding its k nearest neighbors. A framework of using MSLIM for item recommendation is proposed accordingly. The experimental results on a self-collected handbag dataset show that MSLIM outperforms the selected comparison methods and show how the model parameters affect the final recommendation results.

Most video recommender systems limit the content to the metadata associated with the videos, which could lead to poor results since metadata is not always available or correct. Meanwhile, the visual information of videos is typically not fully explored, which is especially important for recommending new items with limited metadata information. In this paper, a novel content-based video recommendation framework, called Video Topic, that utilizes a topic model is proposed. It decomposes the recommendation process into video representation and recommendation generation. It aims to capture user interests in videos by using a topic model to represent the videos, and then generates recommendations by finding those videos that most fit to the topic distribution of the user interests. Experimental results on the Movie Lens dataset validate the effectiveness of Video Topic by evaluating each of its components and the whole framework.

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.

Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.

Designing an optimal dosing regimen for the systemic opioid remifentanil during labor necessitates the prediction of the pace of contractions, so that the drug can be given shortly before the pain of the contraction begins. The prediction and drug administration should be made early enough to allow for the administration of intravenous analgesia that will have maximal efficacy during contractions and little effect between contractions. Towards such a need, we propose a knowledge-assisted sequential pattern analysis framework to predict the changes in intrauterine pressure, which indicate the occurrence of labor contractions. In particular, a patient selection strategy is proposed to select a group of patients, from the stored record, who share similar demographic and obstetrical information with the current patient of interest. A sequential association rule mining approach is designed to learn the patterns of the contractions from the historical patient tracings, and to determine which demographic and obstetrical features have an impact on the contraction patterns. The promising experimental results show that the proposed framework is effective, robust, and efficient in predicting the labor contraction patterns.

Automatic high-level semantic concept detection is a crucial step for multimedia data management, indexing, and retrieval. It is well-acknowledged that semantic gap poses a great challenge in multimedia content-based research. It becomes even more challenging when the concept of interest is extremely rare in the training data sets because of the poor modeling for the positive instances. In this paper, a Concept Association Network (CAN) is trained by selecting significant links to capture the strong associations among different concepts using association rule mining (ARM). By taking into account of the correlations and credibilities of reference concept nodes, the advantages of the reference nodes are utilized. Experimental results using TRECVID 2010 data sets show that by utilizing the proposed framework, the Mean Average Precision (MAP) values of all the concepts are improved, and the significant improvement of the MAP values of the rare concepts further attests the promising results.

In the area of multimedia semantic analysis and video retrieval, automatic object detection techniques play an important role. Without the analysis of the object-level features, it is hard to achieve high performance on semantic retrieval. As a branch of object detection study, moving object detection also becomes a hot research field and gets a great amount of progress recently. This paper proposes a moving object detection and retrieval model that integrates the spatial and temporal information in video sequences and uses the proposed integral density method (adopted from the idea of integral images) to quickly identify the motion regions in an unsupervised way. First, key information locations on video frames are achieved as maxima and minima of the result of Difference of Gaussian (DoG) function. On the other hand, a motion map of adjacent frames is obtained from the diversity of the outcomes from Simultaneous Partition and Class Parameter Estimation (SPCPE) framework. The motion map filters key information locations into key motion locations (KMLs) where the existence of moving objects is implied. Besides showing the motion zones, the motion map also indicates the motion direction which guides the proposed integral density approach to quickly and accurately locate the motion regions. The detection results are not only illustrated visually, but also verified by the promising experimental results which show the concept retrieval performance can be improved by integrating the global and local visual information.

Data management support to enable effective and efficient information sharing in collaborative environments is critical, especially in semantics based search and retrieval. In this paper, a novel spectrum perturbation-based subspace classification is proposed to mine semantics and other useful i

Semantic information retrieval is a popular research topic in the multimedia area. The goal of the retrieval is to provide the end users with as relevant results as possible. Many research efforts have been done to build ranking models for different semantic concepts (or classes). While some of them have been proven to be effective, others are still far from satisfactory. Our observation that certain target semantic concepts have high co-occurrence relationships with those easy-to-retrieve semantic concepts (or called reference semantics) has motivated us to utilize such co-occurrence relationships between semantic concepts in information retrieval and re-ranking. In this paper, we propose a novel semantic retrieval and re-ranking framework that takes advantage of the co-occurrence relationships between a target semantic concept and a reference semantic concept to re-rank the retrieved results. The proposed framework discretizes the training data into a set of feature-value pairs and employs Multiple Correspondence Analysis (MCA) to capture the correlation in terms of the impact weight between feature-value pairs and the positive-positive class in which the data instances belong to both the target semantic concept and the reference semantic concept. A combination of all these impact weights is utilized to re-rank the retrieved results for the target semantic concept. Comparative experiments are designed and evaluated on TRECVID 2005 and TRECVID 2010 video collections with public-available ranking scores. Experimental results on different retrieval scales demonstrate that our proposed framework can enhance the retrieval results for the target semantic concepts in terms of average precision, and the improvements for some semantic concepts are promising.

It is a great challenge to detect an object that is overlapped or occluded by other objects in images. For moving objects in a video sequence, their movements can bring extra spatio-temporal information of successive frames, which helps object detection, especially for occluded objects. This paper proposes a moving object detection approach for occluded objects in a video sequence with the assist of the SPCPE (Simultaneous Partition and Class Parameter Estimation) unsupervised video segmentation method. Based on the preliminary foreground estimation result from SPCPE and object detection information from the previous frame, an n-steps search (NSS) method is utilized to identify the location of the moving objects, followed by a size-adjustment method that adjusts the bounding boxes of the objects. Several experimental results show that our proposed approach achieves good detection performance under object occlusion situations in serial frames of a video sequence.

The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

Natural disasters, such as hurricanes, have had an enormous social and economical impact on society in the United State and around the world for many years. With the goal of preventing, diverting, or weakening the destructive forces of tropical cyclones, the preparedness of the public plays a major role in the magnitude of inflicted damage due to these storms. Acknowledging the captivating power of social networking and Web 2.0 over society, we present a prototype system which integrates meteorological data along with user generated content with the aim of improving public response by increasing their situational awareness due to such natural threats. The proposed system aggregates storm track and wind analysis data from the existing H*Wind system along with videos taken from YouTube and presents it to the user in Google Earth. A content-based concept detection mechanism is used to evaluate the relevance of the extracted YouTube videos to the storm of interest. The proposed system demonstrates the potential public benefit resulting from the integration of the areas of multimedia content analysis, Web 2.0, and meteorology.

Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. This paper aims at utilizing potential benefits from applying distributed multiagent technology to network security. To facilitate information exchange between different agents in our proposed hybrid layered multiagent architecture, a low-cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that our proposed system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time.

Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with a new associative classification algorithm which generates the classification rules based on the correlation between different feature-value pairs and the concept classes by using multiple correspondence analysis (MCA). Experimenting with the high-level features and benchmark data sets from TRECVID, our proposed algorithm achieves promising performance and outperforms three well-known classifiers which are commonly used for performance comparison in the TRECVID community.

With the rapid increase in the amount of multimedia data, the researches on semantic information retrieval are facing a very challenging problem - the number of positive data instances with the target concept/object/event compared with the number of negative data instances without the target concept/object/event is much smaller, which is also called the data imbalance issue. Therefore, one of the popular topics in multimedia information processing and retrieval is data pruning, a technique that can automatically identify and prune the data instances from the training data set so that the pruned data set is able to enhance the performance of model learning, classification, and concept detection. In this paper, a novel data pruning framework which gives each transaction a weight based on multiple correspondence analysis (MCA) is proposed. These transaction weights are used as the measure for pruning the training data set. Meanwhile, the testing data set could be weighted and pruned as well so that the computational cost is reduced not only when building the model but also when applying the classifiers. Experimenting with 18 high-level concepts and the benchmark (both balanced and imbalanced) data sets from TRECVID, our proposed framework achieves promising results to enhance the concept detection performance of three well-known classifiers commonly used for concept detection.

802.11 WLAN technology has been widely used recently to transmit heterogeneous data. The transmission of multimedia data has its quality of service (QoS) requirements. Hybrid Coordination Function (HCF) is proposed to provide service differentiation to support real-time transmission by 802.11e working group. HCF is composed of connection-based Enhanced Distributed Coordination Access (EDCA) and contention-free control-based HCF Controlled Channel Access (HCCA). The simple scheduler in HCCA proposed by 802.11e working group generates constant bit rate service, which is not efficient for the multimedia data with variable bit rates. An efficient scheduling scheme for the 802.11e wireless LAN is proposed in this paper. The allocation of the transmission opportunity to each wireless station is based on the optimization performance index and uses the queue length of the wireless station as feedback information. Simulation results demonstrate that the proposed scheduling can achieve a better QoS for different traffic class under heavy traffic load.

Semantic concept detection has emerged as an intriguing topic in multimedia research recently. The ability to interpret high-level semantics from low-level features has been the long desired goal of many researchers. In this paper, we propose a novel framework that utilizes the ability of multiple correspondence analysis (MCA) to explore the correlation between different items (feature-value pairs) and classes (concepts) to bridge the gap between the extracted low-level features and high-level semantic concepts. Using the concepts and benchmark data identified and provided by the TRECVID project, we have shown that our proposed framework demonstrates promising results and performs better than the decision tree (DT),support vector machine (SVM), and naive Bayesian (NB) classifiers that are commonly applied to the TRECVID datasets.

The current Internet servers are susceptible to network attacks. The DDoS attacks consume the network bandwidth and degrade the services provided by the servers. This paper proposed a mechanism to provide security and quality of service (QoS) for a server pool with the support at the edge router. It focuses on the protection of the services based on the priority of different traffic flows and the anomaly degrees of the traffic. Anomaly traffic will be detected using a lightweight anomaly detection method. The result of anomaly detection is sent to the queue management component for resource allocation. Traffic flows are treated with different priorities. The multimedia flows are guaranteed the bandwidth allocation; while the bandwidth allocated for anomaly traffic flows are restricted according to the degrees of the anomaly. Simulation results demonstrate the improvement of service provision for the legitimate traffic flows under a DDoS attack.

In this paper, a Role-based Access Control (RBAC) model is applied and extended to a multimedia version called Multi-Role Based Access Control (MRBAC), which can fully support the comprehensive and multi-level security control requirements of the distributed multimedia applications. The object-oriented concept is adopted in MRBAC to perform the hybrid role hierarchy management and security roles and rules administration. In summary, MRBAC can: 1) support the multi-level security protection for multimedia data; 2) provide access control by checking both the time constrains and IP addresses; and 3) decentralize the administration functions to make the access control management more efficient.

In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e.,semantic gapandrare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.

Digital audio and video have recently taken a center stage in the communication world, which highlights the importance of digital media information management and indexing. It is of great interest for the multimedia research community to find methods and solutions that could help bridge the semantic gap that exists between the low-level features extracted from the audio or video data and the actual semantics of the data. In this paper, we propose a novel framework that works towards reducing this semantic gap. The proposed framework uses the a priori algorithm and association rule mining to find frequent itemsets in the feature data set and generate classification rules to classify video shots to different concepts (semantics). We also introduce a novel pre-filtering architecture which reduces the high positive to negative instances ratio in the classifier training step. This helps reduce the amount of misclassification errors. Our proposed framework shows promising results in classifying multiple concepts.

Multimedia transmission over the Internet has its quality of service (QoS) requirement. However, denial-of-service (DoS) attacks launch large volumes of traffic and consume network bandwidth, thus degrading the quality of the delivered multimedia service. In this paper, we present a differentiated service protection framework consisting of anomaly traffic detection and resource management. Data mining based anomaly traffic detection is implemented at the host; whereas resource management is responsible for the allocation of network resources to the applications. Based on the deviation of the monitored traffic to the normal traffic, differentiated resources are allocated to the traffic. When suspicious traffic is detected, multimedia transmission reacts proactively via adjusting the transmission rate, which can avoid possible packet loss resulting from DoS bandwidth consumption, and thus provide better QoS. The impact of the traffic anomalies on the legitimate services can be mitigated, and the legitimate services can be protected. Simulation results show that better PSNR can be achieved for multimedia transmission during an attack in our proposed framework.

In this paper, the rare event detection issue in video event detection is addressed through the proposed data mining framework which can be generalized to be domain independent. The fully automatic process via the combination of distance-based and rule-based data mining techniques can greatly reduce the number of negative (non-event) instances and the feature dimension to facilitate the final event detection, without pruning away any positive (event) testing instance along the process. The effectiveness and efficiency of the proposed framework are demonstrated over the goal event detection application based on a large collection of soccer videos with different styles.

Recently, network security has become an extremely vital issue that beckons the development of accurate and efficient solutions capable of effectively defending our network systems and the valuable information journeying through them. In this article, a distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to provide an accurate and lightweight solution to network intrusion detection by tackling issues associated with the design of a distributed multiagent system, such as poor system scalability and the requirements of excessive processing power and memory storage. The proposed IDS architecture consists of (i) the Host layer with lightweight host agents that perform anomaly detection in network connections to their respective hosts, and (ii) the Classification layer whose main functions are to perform misuse detection for the host agents, detect distributed attacks, and disseminate network security status information to the whole network. The intrusion detection task is achieved through the employment of the lightweight Adaptive Sub-Eigenspace Modeling (ASEM)-based anomaly and misuse detection schemes. Promising experimental results indicate that ASEM-based schemes outperform the KNN and LOF algorithms, with high detection rates and low false alarm rates in the anomaly detection task, and outperform several well-known supervised classification methods such as C4.5 Decision Tree, SVM, NN, KNN, Logistic, and Decision Table (DT) in the misuse detection task. To assess the performance in a real-world scenario, the Relative Assumption Model, feature extraction techniques, and common network attack generation tools are employed to generate normal and anomalous traffic in a private LAN testbed. Furthermore, the scalability performance of the proposed IDS architecture is investigated through the simulation of the proposed agent communication scheme, and satisfactory linear relationships for both degradation of system response time and agent communication generated network traffic overhead are achieved.

Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.

In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms

Event detection is of great importance in high-level semantic indexing and selective browsing of video clips. However, the use of low-level visual-audio feature descriptors alone generally fails to yield satisfactory results in event identification due to the semantic gap issue. In this paper, we propose an advanced approach for exciting event detection in soccer video with the aid of multi-level descriptors and classification algorithm. Specifically, a set of algorithms are developed for efficient extraction of meaningful mid-level descriptors to bridge the semantic gap and to facilitate the comprehensive video content analysis. The data classification algorithm is then performed upon the combination of multimodal mid-level descriptors and low-level feature descriptors for event detection. The effectiveness and efficiency of the proposed framework are demonstrated over a large collection of soccer video data with different styles produced by different broadcasters.

A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns-especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework

The focal goal of our research is to develop a general framework which can automatically analyze the sports video, detect the sports events, and finally offer an efficient and user-friendly system for sports video retrieval. In our earlier work, a novel multimedia data mining technique was proposed for automatic soccer event extraction by adopting multimodal feature analysis. Until now, this framework has been performed on the detection of goal and corner kick events and the results are quite impressive. Correspondingly, in this work, the detected video events are modeled and effectively stored in the database. A temporal query model is designed to satisfy the comprehensive temporal query requirements, and the corresponding graphical query language is developed. The advanced characteristics make our model particularly well suited for searching events in a large scale video database.

In this paper, we propose a multi-buffer scheduling scheme for streaming video systems. A transmission rate is obtained via a rate control algorithm, which optimally utilizes the network bandwidth and client buffer resources. The server side maintains multiple buffers for packets of different importance levels. It schedules the transmission of each packet based on the source buffer size and playback deadline to reduce the end-to-end distortion. The performance of proposed scheme is evaluated in terms of peak signal-to-noise ratio (PSNR) in the simulations, and the simulation results demonstrate the improvement of the average PSNR values compared with two other scheduling schemes.

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm has been shown through comparative studies.

In this paper, a secure and adaptive multimedia transmission framework is proposed to maintain the quality of service (QoS) of the multimedia streams during the denial-of service (DoS) attacks. A DoS attack generates a large amount of traffic, which will occupy the bandwidth and degrade the quality of video. The proposed framework consists of two components: intrusion detection and adaptive transmission management. The intrusion detection component monitors the incoming traffic to the server, detects the attacks, and interacts with the adaptive transmission management component; while the adaptive transmission management component is designed to improve QoS of the video via the efficient utilization of the network resources. With the detection of the DoS attacks, the bandwidth occupied by the attacks can be reduced and protected for video transmission. The results of our preliminary simulations in NS2 show that the quality of the multimedia stream can still be maintained during an attack.

In this paper, a multimodal unit detection framework to detect and extract units, a novel concept towards event detection and extraction in sports TV broadcasts, is proposed. The proposed unit is defined to be a segment of a sports TV broadcast that describes a potentially interesting event, which possesses the potential of attracting the attention of the observer and satisfy his/her need of viewing the more interesting segments of the broadcast. A number of events that are considered as the unit target events are game impacting events such as score, missed score, penalties, and special game inserts, such as highlights and statistics clips. The proposed framework serves as an efficient data preprocessing procedure that can reduce the amount of data by ridding off the irrelevant data and prepare the remaining data in an efficient way for future event detection and extraction. Several experiments are conducted on various football games from different TV broadcasts, including college football and professional football. The experimental results demonstrate that the proposed framework effectively achieves the goal of data reduction, which is expected to increase the accuracy of event detection and extraction from the American football TV broadcast.

In this paper, a neural network based framework for semantic event detection in soccer videos is proposed. The framework provides a robust solution for soccer goal event detection by combining the strength of multimodal analysis and the ability of neural network ensembles to reduce the generalization error. Due to the rareness of the goal events, the bootstrapped sampling method on the training set is utilized to enhance the recall of goal event detection. Then a group of component networks are trained using all the available training data. The precision of the detection is greatly improved via the following two steps. First, a pre-filtering step is employed on the test set to reduce the noisy and inconsistent data, and then an advanced weighting scheme is proposed to intelligently traverse and combine the component network predictions by taking into consideration the prediction performance of each network. A set of experiments are designed to compare the performance of different bootstrapped sampling schemes, to present the strength of the proposed weighting scheme in event detection, and to demonstrate the effectiveness of our framework for soccer goal event detection.

We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.

As the development of the Internet continues, congestion control has become a big issue to the computer network society. Most congestion control schemes fall into two categories, end-to-end and hop-by-hop schemes. We propose a novel hop-by-hop algorithm that originates from a classical traffic control algorithm. The experimental results show that our proposed algorithm can achieve short delays and quick responses to the congestion situations and cause no packet loss. It can also minimize the bandwidth requirement and achieve a very high buffer usage level for nodes along the transmission path.

We present a new framework for streaming video over Internet in this paper. The sending rate is dynamically adjusted to obtain a maximal utilization of the client buffer and minimal bandwidth allocation. To guarantee a more reliable and better quality of delivery of the video frames, retransmission and selective drop of different frame packets are integrated into our framework. Under severe network congestion, an adaptive playback schedule can provide relative high video quality at a low frame rate. Comparisons are made with the most current streaming approaches using the H.26L video coder to evaluate the performance of the framework. Simulations results show that PSNR is increased in our approach, which provides a better quality of the decoded frames, and the quality of the decoded frames also changes more smoothly.

A novel active queue management scheme is proposed to employ in the routers that deal with both the best-effort traffic flows and multimedia traffic flows. Most of the available active queue management schemes consider only TCP flows, which results in unexpected congestion when dealing with multimedia flows. The queue size and packet receiving rate are the common parameters to calculate the marking probability in the queue. The use of round trip time (RTT) in the process of marking probability calculation has advantages over other approaches. We discuss the applicability of our approach when the framework in our earlier paper is used for rate adaptation in multimedia flows. With the use of RTT in packet marking probability calculations, we can assure rate reduction in both best-effort and multimedia flows before a queue gets exhausted. The most influential feature of the proposed approach is that it can work with the existing systems. The soundness of the proposed approach is inferred from the simulation results.

The paper proposes a method to discover effectively users' concept patterns when multiple objects of interest (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates multiple instance learning into the user relevance feedback in a seamless way to discover where the user's objects/regions of most interest are and how to map the local features of that(those) region(s) to the user's high-level concepts. A three-layer neural network is used to model the underlying mapping progressively through the feedback and learning procedure.

In this paper, a framework named SMARXO is proposed to address the security issues in multimedia applications by adopting RBAC (Role-Based Access Control), XML, and Object-Relational Databases. Compared with the other existing security models or projects, SMARXO can deal with more intricate situations. First, the image object-level security and video scene/shot-level security can be easily achieved. Second, the temporal constrains and IP address restrictions are modeled for the access control purpose. Finally, XML queries can be performed such that the administrators can proficiently retrieve useful information from the security roles and policies.

In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.

The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.

In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.

This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.

In this paper, we propose a rate control mechanism for multimedia multicast streaming in the Internet. A single and optimal transmission rate that is adaptive to the network congestion, buffer occupancies, and the playback requirements of the member clients in the multicast group is allocated. We optimize the multicasting delivery in the Internet by improving the buffer occupancy of all clients for on-time presentation while using the minimal transmission rate. Scalable playback is used for the clients with low bandwidth capabilities in a heterogeneous environment. Simulation results show that the buffer capacity can be efficiently improved and fairness is ensured among the member clients.

In this paper, we propose a per-class queue management and adaptive packet drop mechanism in the routers for Internet congestion control. We model active queue management as an optimization problem and our proposed mechanism provides congestion control and fairness for different types of traffic flows. An optimal packet drop rate is obtained to maintain a relatively small queue occupancy, which provides a less queue delay delivery of packets. Simulations are conducted to compare our approach with the fixed packet drop rate approach. Moreover, the queue occupancy and the packet drop rates obtained are both upper bounded, which is meaningful for providing the class-based guaranteed delay services for real-time multimedia applications.

In this demonstration, we present an image retrieval system to support multimedia authoring and presentation. An affinity-based mechanism, Markov Model Mediator (MMM), is used as the search engine for the system, which utilizes both the low-level image features and the learned high-level concepts via user access patterns and access frequencies. This system is one of the major components of MediaManager, a distributed multimedia management system developed by us. Both retrieval and learning facilities are supported in this system. The retrieval system also provides input information to the Multimedia Augmented Transition Network (MATN) environment for multimedia authoring and presentation.

This paper addresses the problem of mapping natural language to its semantics. It presupposes that the input is in random (compressed) form and proceeds to detail a methodology for extracting the semantics from that normal form. The idea is to enumerate contextual cues and learn to associate those cues with meaning. The process is inherently fuzzy and for this reason is also inherently adaptive in nature. It is shown that the influence of context on meaning grows exponentially with the length of a word sequence. This suggests that rule-based randomization plays a key role in rendering a field-effect natural language semantic mapping tractable. An example of rule-based randomization for semantic normalization is as follows. Suppose that two commands to a robot are deemed to be equivalent; namely, "Grasp and pick up the glass" and "Hold the cup and raise it". Their mutual normalization might then be, "Grab container. Lift container." Clearly, the randomization process can be effected by rules. Also, the normalized syntax makes the result of any semantic mapping process-such as detailed herein-more efficient. A natural language front-end is described, which is designed to reduce the impedance mismatch between the human and the machine. Most significantly, the effective translation of natural language semantics is shown to critically depend on an accelerated capability for learning.

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.

One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.

In this paper, a method of automatically classifying web documents into a set of categories using the fuzzy association concept is proposed. Using the same word or vocabulary to describe different entities creates ambiguity, especially in the web environment where the user population is large. To solve this problem, fuzzy association is used to capture the relationships among different index terms or keywords in the documents, i.e., each pair of words has an associated value to distinguish itself from the others. Therefore, the ambiguity in word usage is avoided. Experiments using data sets collected from two web portals: Yahoo! and Open Directory Project are conducted. We compare our approach to the vector space model with the cosine coefficient. The results show that our approach yields higher accuracy compared to the vector space model.

An effective content-based visual image retrieval system is presented. This system consists of two main components: visual content extraction and indexing, and query engine. Each image in the image database is represented by its visual features: color and spatial information. The system uses a color label histogram with only thirteen bins to extract the color information from an image in the image database. A unique unsupervised segmentation algorithm combined with the wavelet technique generates the spatial feature of an image automatically. The resulting feature vectors are relatively low in dimensions compared to those in other systems. The query engine employs a color filter and a spatial filter to dramatically reduce the search range. As a result, queue processing is speeded up. The experimental results demonstrate that our system is capable of retrieving images that belong to the same category.

Automatic video scene change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently still remains a difficult issue since there are various cases in their relationship due to the versatility of videos. We present an effective scene change detection method that adopts the joint evaluation of the audio and visual features. First, video information is used to find the shot boundaries. Second, the audio features for each video shot can be extracted. Lastly, an audio-video combination schema is proposed to detect the video scene boundaries.

Recently, efficient network resource management and quality-of-service (QoS) guarantee become more and more important for multimedia applications and services, especially when considering network delays. An optimal bandwidth allocation scheme is introduced. It achieves maximal utilization of the client buffer and minimal allocation of bandwidth for each client. The proposed scheme allocates bandwidth to multiple clients requesting services from a server by adjusting the transmission rates based on the client buffer occupancy, the playback requirements of the individual client and the network delays. Simulations for the single client and multiple client scenarios are conducted under different network congestion levels. The simulation results show that our approach performs better in comparison with the fixed rate allocation approach and the rate by playback requirement approach, since it avoids underflows and overflows efficiently and provides QoS for more clients with limited available bandwidth in the network.

Due to the explosive growth of available information on the World Wide Web (WWW), users have suffered from the information overload. To alleviate this problem, there is a need for an intelligent tool to help the users screening and filtering for interesting and useful information. In this paper, a method of automatically identifying topics for Web documents via a classification technique is proposed. Topic identification can be applied as a filtering tool for recommender systems to prune down the number of documents to within some particular topics. We adopt the fuzzy association concept as a machine learning technique to classify the documents into some predefined categories or topics. Our approach is compared to the vector space model with the cosine coefficient using the data sets collected from three different Web portals: Yahoo!, Open Directory Project and Excite. The results show that our approach yields higher classification accuracy compared to the vector space model.

In this paper a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.

In order to manage the growing amount of video information efficiently, a video scene change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the scene change detection to organize the video content. In this paper, we present an effective scene change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate scene change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis. 1.

Efficient utilization of network resources is essential in the provision of quality multimedia services. Different approaches have been proposed to shape the multimedia streams as a transmission schedule with smoothed traffic burst. In this paper, we concentrate on the problem of efficient use of bandwidth and client buffer. For this purpose, an optimal transmission schedule is proposed to provide the minimal allocation of the bandwidth and maximal utilization of the client buffer. Simulation results show that the shaping results obtained can dynamically adjust the transmission rate according to the buffer packet sizes and playback rates at the client to avoid the loss of packets, and at the same time achieve the minimal bandwidth allocation and maximal utilization of the client buffer. 1.

In today's fast-growing information age, multimedia data is becoming more and more common in daily applications, however this data is nearly useless if there is no computer-aided browsing, searching, and retrieving mechanism to obtain the desired contents. In this demonstration, we present a multimedia query and presentation management system for multimedia databases. It uses an unsupervised segmentation method for image and video feature extraction. A presentation design interface systems is provided that allows query results to be used to create multimedia presentations.

In this paper, an affinity-based approach that provides good similarity measures for Web document clustering to discover user access behavior on the World Wide Web (WWW) is proposed. The proposed approach generates the similarity measures for groups of Web documents by considering the user access patterns. Any clustering algorithm using better similarity measures should yield better clusters for discovering user access behavior. By utilizing the discovered user access behavior, for example, the companies can precisely target their potential customers and convince them to purchase their products or services in electronic commerce. An experiment on a real data set is conducted and the experimental result shows that the proposed approach yields a better performance than the cosine coefficient and the Euclidean distance method under the partitioning around medoid (PAM) method.

The World Wide Web (WWW) has become one of the fastest growing applications on the Internet today. More and more information sources have linked online through WWW, but finding information on the WWW is also a great challenge. For most of the users, the information retrieved is not well organized and the access time is considered high on the WWW currently. Therefore, there is a need to develop a good mechanism to organize and manage the tremendous size and various kinds of information to facilitate the functionality of a search engine for information retrieval on the WWW. In response to such a demand, we propose a Markov Model Mediator (MMM) mechanism which employs the affinity-based data mining techniques to organize and manage the information sources so that the most relevant documents are clustered together to achieve higher recall and precision values for information retrieval on the WWW. 1 Introduction Since its introduction in the early 1990s, the World Wide Web (WWW) has becom...

S.C. Chen et al. (1999) proposed a multimedia augmented transition network (ATN) model, together with its multimedia input strings, to model and structure video data. This multimedia ATN model was based on an ATN model that had been used within the artificial intelligence (AI) arena for natural-language understanding systems, and its inputs were modeled by multimedia input strings. The temporal and spatial relations of semantic objects were captured by an unsupervised video segmentation method called the SPCPE (simultaneous partitioning and class parameter estimation) algorithm, and they were modeled by the multimedia input strings. However, the segmentation method used was not able to identify objects that are overlapped together within video frames. The identification of overlapped objects is a great challenge. For this purpose, a backtrack-chain-update-split algorithm is developed in this paper that identifies the split segment (object) and uses this information in the current frame to update the previous frames in a backtrack-chain manner. The proposed split algorithm provides more accurate temporal and spatial information of the semantic objects for video indexing.

This paper presents a presentation semantic model that is based on the augmented transition network (ATN) for asynchronous distance learning system called Java-based Integrated Asynchronous Distance Learning (JIADL) system. The JIADL system can support diverse asynchronous distance learning services. Unlike most related work in the literature, we integrate RealPlayer and Java technology so that the superiority of both models can be complemented. A course sample is illustrated to validate the effectiveness of the paradigm proposed. How to use the proposed multimedia ATN model to model the diverse requirements of a distance learning multimedia presentation is also discussed. The multimedia ATN model is powerful in modeling the asynchronization for distance learning multimedia presentations. Furthermore, in addition to supporting asynchronous distance learning, our system can be applied to a wide range of potential value-added applications.

A distributed database is a collection of data sources distributed across many computers. It is a single logical and centrally managed database stored in multiple physical locations. In order to access data locally and manage data globally, a good distributed database system (DDBS) that can supply reliable and up-to-date information is critical. A good DDBS should have functionalities such as providing users with timely and flexible access to information, providing DBA (database administrator) personnel with tools to analyze the data in a meaningful manner, and allowing the personnel to control the safety and integrity of the data. However, query processing in such a distributed database system is complicated. For example, some important issues are how to keep data up-to-date when it is physically dispersed, and how to propagate information to the whole DDBS when an update occurs. In response to these issues, we propose an expert query system that uses a Bayesian network as its framework to propagate information and to keep data up-to-date for a DDBS. The goal of this query system is to assist the DBA personnel in maintaining the data dynamically and to assist the users in accessing reliable and timely information.

Due to the complexity of real-world applications, the number of databases and the volumes of data in databases have increased tremendously. With the explosive growth in the amount and complexity of data, how to effectively organize the databases and utilize the huge amount of data becomes important. For this purpose, a probabilistic network that organizes a network of databases and manages the data in the databases is proposed. Each database is represented as a node in the probabilistic network and the affinity relations of the databases are embedded in the proposed Markov model mediator (MMM) mechanism. Probabilistic reasoning is used to formulate and derive the probability distributions for an MMM. Once the probability distributions of each MMM are generated, a stochastic process is conducted to calculate the similarity measures for pairs of databases. The similarity measures are transformed into the branch probabilities of the probabilistic network. Then, the data in the database can be managed and utilized to allow user queries for database searching and information retrieval. An example is included to illustrate how to model each database into an MMM and how to organize the network of databases into a probabilistic network.

Association rule mining has recently attracted strong attention and proven to be a highly successful technique for extracting useful information from very large databases. In this paper, we explore a generalized affinity-based association mining which discovers quasi-equivalent media objects in a distributed information-providing environment consisting of a network of heterogeneous databases which could be relational databases, hierarchical databases, object-oriented databases, multimedia databases, etc. Online databases, consisting of millions of media objects, have been used in business management, government administration, scientific and engineering data management, and many other applications owing to the recent advances in high-speed communication networks and large-capacity storage devices. Because of the navigational characteristic, queries in such an information-providing environment tend to traverse equivalent media objects residing in different databases for the related data records. As the number of databases increases, query processing efficiency depends heavily on the capability to discover the equivalence relationships of the media objects from the network of databases. Theoretical terms along with an empirical study of real databases are presented.

In an interactive multimedia information system, users should have the flexibility to browse and choose various scenarios they want to see. This means that two-way communications should be captured by the conceptual model. Digital video has gained increasing popularity in many multimedia applications. Instead of sequential access to the video contents, the structuring and modeling of video data so that users can quickly and easily browse and retrieve interesting materials has become an important issue in designing multimedia information systems. An abstract semantic model called the augmented transition network (ATN), which can model video data and user interactions, is proposed in this paper. An ATN and its subnetworks can model video data based on different granularities, such as scenes, shots and key frames. Multimedia input strings are used as inputs for ATNs. The details of how to use multimedia input strings to model video data are also discussed. Key frame selection is based on the temporal and spatial relations of semantic objects in each shot. These relations are captured from our proposed unsupervised video segmentation method, which considers the problem of partitioning each frame as a joint estimation of the partition and class parameter variables. Unlike existing semantic models, which only model multimedia presentation, multimedia database searching or browsing, ATNs together with multimedia input strings can model these three in one framework.