Most state-of-the-art local interpretation methods explain the behavior of deep learning classification models by assigning importance scores to image pixels based on how influential each pixel was towards the final decision. These interpretations are unable to provide further details to aid understanding of a complex concept in a domain such as medicine. We propose a novel Hierarchical Visual Concept (HVC) interpretation framework for CNN-based image classification models. As an explanation of the classification decision of a given image, HVC presents a concept hierarchy of most relevant visual concepts at multiple semantic levels. These concepts are automatically learned during training such that the lower-level concepts in the hierarchy support the corresponding higher-level concepts. Our quantitative and qualitative evaluation of the interpretation of VGG16 and ResNet50 classifiers on public and private colonoscopy image datasets shows very promising results.

In this research work, we propose a content-based approach MATURE, utilizing the additional information about both the user and item to predict the items for such a user who has current mandatory needs to accommodate as opposed to the past preferences. To the best of our knowledge, MATURE is the first such recommender system that guarantees and ensures the inclusion of the specified mandatory features, when recommending the items to the user and justifies the recommendation by using the information enclosed in mandatory features.

We report the technical challenges, solutions, and lessons learned from deploying real-time feedback systems in three hospitals as part of a multi-center controlled clinical trial to improve quality of colonoscopy. Previous clinical trials were conducted in one center. The technical challenges for our multicenter clinical trial include 1) reducing additional work by the endoscopists to utilize real-time feedback, 2) handling different colonoscopy practices at different hospitals, and 3) training an effective CNN-based classification model with a large variety of patterns of data in day-to-day colonoscopy practice. We report performance of our real-time systems over a period of 20 weeks at each hospital. We conclude that CNN-based classification can achieve very good performance in real-world deployment when trained with high quality data.

Many image classification tasks (e.g., medical image classification) have a severe class imbalance problem. Convolutional neural network (CNN) is currently a state-of-the-art method for image classification. CNN relies on a large training dataset to achieve high classification performance. However, manual labeling is costly and may not even be feasible for medical domain. In this paper, we propose a novel similarity-based active deep learning framework (SAL) that deals with class imbalance. SAL actively learns a similarity model to recommend unlabeled rare class samples for experts' manual labeling. Based on similarity ranking, SAL recommends high confidence unlabeled common class samples for automatic pseudo-labeling without experts' labeling effort. To the best of our knowledge, SAL is the first active deep learning framework that deals with a significant class imbalance. Our experiments show that SAL consistently outperforms two other recent active deep learning methods on two challenging datasets. What's more, SAL obtains nearly the upper bound classification performance (using all the images in the training dataset) while the domain experts labeled only 5.6% and 7.5% of all images in the Endoscopy dataset and the Caltech-256 dataset, respectively. SAL significantly reduces the experts' manual labeling efforts while achieving near optimal classification performance.

The explosive use of twitter in the political landscape presents new avenues for tracking political conversations at federal and state level. Tweets are used by state and federal government bodies to present citizens with information about future and present policies. It is also used by political candidates to express their views on policy changes, laws and to campaign for legislative body elections, the most recent example being the 2016 US presidential elections. In this paper, we use supervised learning, textual semantic similarity and community detection techniques to find actively discussed policy agenda sub-topics among political tweets within a certain time period. Specifically, we target tweets pertaining to major policy agendas published by state representatives in US, to try and discern the major policy sub-topics that they address using their twitter accounts. Using our method, we demonstrate how we achieve a high accuracy in terms of Topic Recall and Order Recall, by comparing the output of our proposed method with sub-topic annotations done by domain experts.

Twitter is a popular online microblogging service that has become widely used by politicians to communicate with their constituents. Gaining understanding of the influence of Twitter in state politics in the United States cannot be achieved without proper computational tools. We present the first attempt to automatically classify tweets of state legislatures (policy makers at the state level) into major policy agenda topics defined by Policy Agendas Project (PAP), which was initiated to group national policies. We investigated the effectiveness of three popular machine learning algorithms, Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory Network (LSTM). We proposed a new synthetic data augmentation method to further improve classification performance. Our experimental results show that CNN provides the best F1 score of 78.3%. The new data augmentation method improves the classification perfromance by about 2%. Our tool provides a good prediction of the top three popular PAP topics in each month, which is useful for tracking popular PAP topics over time and across states and for comparing with national policy agendas.

We describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to collect a large training dataset: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts. Convolutional Neural Network (CNN) analysis using the training data collected with similarity-based data augmentation yields an average F1 score within 1% of that of the CNN analysis using a large manually collected training dataset.

Presents a listing of the Symposium organizing committee.

This paper presents a novel technique for automated detection of protruding polyps in colonoscopy images using edge cross-section profiles (ECSP). We propose a part-based multiderivative ECSP that computes derivative functions of an edge cross-section profile and segments each of these profiles into parts. Therefore, we can model or extract features suitable for each part. Our features obtained from the parts can effectively describe complex properties of protruding polyps including the shape of the parts, texture, and protrusion and smoothness of the polyp surface. We evaluated our method against two existing polyp image detection techniques on 42 different polyps, including those with little protrusion. Each polyp has a large variation of appearance in viewing angles, light conditions, and scales in different images. The evaluation showed that our technique outperformed the existing techniques in both accuracy and analysis time. Our method has a higher area under the free-response receiver operating characteristic curve. For instance, when both techniques have a true positive rate for polyp image detection of 81.4%, the average number of false regions per image of our technique is 0.32 compared to 1.8 of the best existing technique under study. Additionally, our technique can precisely mark edges of candidate polyp regions as visual feedback. These results altogether indicate that our technique is promising to provide visual feedback of polyp regions in clinical practice.

We refer to a vehicle pool as a number of vehicles at a single location used for the same purpose. We focus on the problem of sizing vehicle pools for a finite set of subscribers who can use the pool. Our goal is to minimize the number of vehicles in the pool while still meeting nearly all subscriber requests. Formally, we propose three analytical techniques to size a vehicle pool for a finite population of subscribers, according to the pools' busy period demand to guarantee all requests are served with probability 1 - ε, i.e., a quality-of-service (QOS) guarantee. Moreover, we propose an additional heuristic sizing method, which requires no prior data about pool demand. Although this method does not provide probabilistic bounds on QOS, we show in practice that it still achieves a high QOS. We evaluate our sizing methodologies using seven years of data from a local car share, using three performance metrics: availability (percentage of requests served), utilization (the percentage of time that vehicles in the pool are used) and member-to-vehicle ratio (the size of the pool relative to the size of its user population). We show that our methods perform well with respect to these metrics.

As cyber threats increasingly utilize automated and adaptive attacks to bypass or overwhelm static defenses, the role of intrusion detection and response systems (IDRS) as an active defense layer is becoming more critical. To remain effective against current attacks IDRS must be capable of automating detection of, and response to, threats in their specific environment. Different operating characteristics, detection capabilities, and response actions all contribute to make each environment unique, complicating this automation. In this work we consider IDRS automation in three areas: detector tuning, detector correlation, and response selection. We motivate and present a novel, more finely-grained model of threats, detectors, and responses called S-MAIDS: A Semantic Model of Automated Intrusion Detection Systems. Based on the concept of a "signal" (an observable indicator of an attack), we show the utility of combining such a model with an existing measure of IDRS performance to facilitate automated tuning, cross-system correlation, and response selection. We support our claims through several case-studies demonstrating the application of this model, and provide the model as an OWL ontology.

Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy helped to detect more polyps. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects the retroflexion during colonoscopy. We propose region shape and location (RSL) features and edgeless edge cross-section profile (ECSP) features that encapsulate important properties of endoscope appearance and edge information during retroflexion. Our experimental results on 50 colonoscopy test videos show that a simple ensemble classifier using both ECSP and RSL features can effectively identify retroflexion in terms of analysis time and detection rate.

In recent years, cost-sensitive intrusion response has gained significant interest mainly due to its emphasis on the balance between potential damage incurred by the intrusion and cost of the response. However, one of the challenges in applying this approach is defining consistent and adaptable measurements of these cost factors on the basis of requirements and policy of the system being protected against intrusions. In this paper we present a framework for the cost-sensitive selection of intrusion response. Specifically, we introduce a set of measurements that characterize potential costs associated with the intrusion handling process and propose evaluation method of intrusion response with respect to the risk of potential intrusion damage, effectiveness of response action and response cost for a system. We provide an implementation of the proposed solution as a plugin tool for Snort IDS and demonstrate its advantages on DARPA data set and real network traffic.

This paper presents SAPPHIRE — a novel middleware and software development kit, developed to reduce implementation efforts in utilizing task parallelism to speed up execution time of analysis of a stream of data, such as medical video. As a case study, we implemented a real-time quality measurement of colonoscopy using SAPPHIRE. We increased the number of threads in our case study from 4 (prior to our middleware) to 28 with ease. As a result, through better load-balancing and taking better advantage of available processor cores, we increased the case study's maximum processing rate from 40 frames per second to 90 frames per second.

Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.

While specialized knowledge and skills are the hallmark of modern society, the size and complexity of contemporary problems often require cooperative effort to analyze and solve. Therefore, experiences with skills, methodologies, and tools for effective interdisciplinary collaboration and structured problem solving are vital for preparing students for future academic and professional success. Meanwhile, computational systems have permeated much of modern professional and personal life, making computational thinking an essential skill for members of modern society. However, formal training in these techniques is primarily limited to students within computer science, mathematics, management of information systems, and engineering. At Iowa State University, we have designed and offered an experimental course to develop undergraduate students' abilities for interdisciplinary teamwork and to disseminate computational thinking skills to a broader range of students. This novel course was jointly designed and instructed by faculty from the Computer Science Department, Gerontology Program, and Graphic Design Program to incorporate diverse faculty expertise and pedagogical approaches. Students were required to interview real users to identify real-life problems, gather requirements, and assess candidate solutions, which necessitated communication both within the group and with technologically-disinclined users. In-class presentations and wiki-based project websites provided regular practice at disseminating domain expertise to larger interdisciplinary audiences. Workshops, group-based mentoring, peer learning, and guided discovery allowed non-CS majors to learn much more about computer programs and tools, and grading criteria held students individually accountable within their disciplines but also emphasized group collaboration

Estimation techniques have been largely applied to track hidden performance parameters (e.g. service demands) of web based software systems. In this paper we investigate dynamic multiclass modeling of such systems, with variable classes of service, aiming at finding a low complexity model yet with enough accuracy. We propose a combination of clustering algorithm and tracking filter for effective grouping of classes of services. The tracking estimator is based on a layered queuing model with parameters for CPU demands and the user load intensity of each class of service. Clustering uses the K-means algorithm. The target application is autonomic control of web clusters, where changes occur at different rates and amplitudes and at random time instants. Experiments show that the tracking is effective, and reveal good filter settings for different variations.

A new algorithm for reconstruction of a 3D virtual colon segment from an individual image captured from colonoscopy is presented. Colonoscopy is currently the gold standard method for detection and prevention of colorectal cancer. However, the protective effect depends on the amount of colon mucosa that is actually seen by the endoscopist. The proposed algorithm takes contours of colon folds in the image as input and calculates the depth and the slant angle of each fold. Finally, the colon mucosa is created using Cubic Bezier Curve interpolation between the folds. The proposed algorithm is an important step toward 3D reconstruction of the virtual colon from video of an entire colonoscopy procedure. The reconstruction is potentially useful to estimate percent of colon mucosa inspected by the endoscopist.

In this paper we present a structured methodology for evaluating cost of responses based on three factors: the response operational cost associated with the daily maintenance of the response, the response goodness that measures the applicability of the selected response for a detected intrusion and the response impact on the system that refers to the possible response effect on the system functionality. The proposed approach provides a consistent basis for response evaluation across different systems while incorporating security policy and properties of the specific system environment.

In this work we proposed a novel heterogeneity-aware group key management framework (HAGK) for wireless mesh networks (WMNs). In our framework a WMN multicast session involves both stationary backbone nodes and mobile client nodes: for backbone nodes which are topologically-stable but may spread over a large area, the distributed thresholdbased group key management technique is applied; for client nodes associated with each backbone node which are mobile but confined in a limited area, the logical key hierarchical technique is applied. The results showed that HAGK reduces rekeying delay and storage overhead at end nodes with minimum communication costs. In addition, the framework is resilient to attacks. Categories and Subject Descriptors

The proliferation of complex and fast-spreading intrusions not only requires advances in intrusion detection mechanisms but also demands development of sophisticated and automated intrusion response systems. In this paper we present a novel cost-sensitive model for intrusion response that incorporates preemptive deployment of the response actions. Specifically, our technique relies on comparing the cost of deploying a response against the cost of damage caused by an "'un-attended" intrusion and decides to preemptively deploy a response with maximum benefit. Our technique further allows adaptation of responses to the changing environment through evaluation of success and failure of previously triggered responses. We demonstrate the advantages of the approach and evaluate it using a damage reduction metric.

Privacy is a known barrier to the acceptance of ubiquitous computing technologies because they require individuals to trade control of their personal information and personal spaces for improved quality of life and assistance with daily activities. Previous work has been done to analyze and protect privacy in ubiquitous computing environments, but such efforts do not include a formal underlying model. We seek to approach the privacy problem from a different perspective. Namely, we seek to propose, verify, and analyze a formal model of privacy for these environments. In this paper, we discuss the beginning stages of our model, namely the resources that need to be protected, the guidelines for constructing the model, and the high-level components of our personal privacy model.

Wormhole attack is a severe attack in wireless ad hoc networks. Most of the previous work eliminate the effect of wormhole attack by examining the distance or communication time over each link during the route establishment, which requires special hardware or causes overhead on all links even though only one link on each route could be affected by a wormhole attack. In this article, we propose an end-to-end detection of wormhole attack (EDWA) in wireless ad-hoc networks. We first present the wormhole detection which is based on the smallest hop count estimation between source and destination. If the hop count of a received shortest route is much smaller than the estimated value an alert of wormhole attack is raised at the source node. Then the source node will start a wormhole TRACING procedure to identify the two end points of the wormhole. Finally, a legitimate route is selected for data communication. Both our analysis and simulation results show that the end-to-end wormhole detection method is effective when the source and destination are not too far away.

Early detection of polyps and cancers is one of the most important goals of colonoscopy. Computer-based analysis of video files using texture features, as has been proposed for polyps of the stomach and colon, has two major limitations: this method uses a fixed size analysis window and relies heavily on a training set of images for accuracy. To overcome these limitations, we propose a new technique focusing on shape instead of texture in this paper. The proposed polyp region detection method is based on the elliptical shape that is common for nearly all small colon polyps.

The integration of Software Fault Tree (SFT), which describes intrusions and Coloured Petri Nets (CPNs) that specifies design, is examined for an Intrusion Detection System (IDS). The IDS under development is a collection of mobile agents that detect, classify, and correlate the system and network activities. SFTs, augmented with nodes that describe trust, temporal and contextual relationships, are used to describe intrusions. CPNs for intrusion detection are built using CPN templates created from the augmented SFTs. Hierarchical CPNs are created to detect critical stages of intrusions. The agentbased implementation of the IDS is then constructed from the CPNs. Examples of intrusions and descriptions of the prototype implementation are used to demonstrate how the CPN approach has been used in the development of the IDS. The main contribution of this paper is an approach to systematic specification, design and implementation of an IDS; Innovations include (1) using stages of intrusions to structure the specification and design of the IDS; (2) augmentation of SFT with trust, temporal and contextual nodes to model intrusions; (3) algorithmic construction of CPNs from augmented SFT; and (4) generation of mobile agents from CPNs.

Recent advances in intrusion detection field brought new requirements to intrusion prevention and response. Traditionally, the response to an attack was manually triggered by an administrator. However, increased complexity and speed of the attack-spread during recent years showed acute necessity for complex dynamic response mechanisms. Although intrusion detection systems are being actively developed, research efforts in intrusion response are still isolated. In this work we present taxonomy of intrusion response systems, together with a review of current trends in intrusion response research. We also provide a set of essential fetures as a requirement for an ideal intrusion response system.

Peer-to-peer (P2P) video systems provide a cost-effective way for a large number of hosts to collaborate for video sharing. Two features characterize such a system: 1) a video is usually available on many participating hosts, and 2) different hosts typically have different sets of videos, though some may partially overlap. From a client's perspective, it can be served by any host having the video it requests. From a server's perspective, it be used to serve any client requesting the videos it has. Thus, an important question is, which servers should be used to serve which clients in the system? In this paper, we refer to this problem as service scheduling and show that different matches between clients and servers can result in significantly different system performance. Finding a right server for each client is challenging not only because a client can choose only the servers that are within its limited search scope, but also because clients arrive at different times, which are not known a priori. In this paper, we address these challenges with a novel technique called Shaking. While the proposed technique makes it possible for a client to be served by a server that is beyond the client's own search scope, it is able to dynamically adjust the match between the servers and their pending requests as new requests arrive. Our performance study shows that our new technique can dynamically balance the system workload and significantly improve the overall system performance

Run-time monitoring is a powerful approach for dynamically detecting faults or malicious activity of software systems. However, there are often two obstacles to the implementation of this approach in practice: (1) that developing correct and/or faulty behavioral patterns can be a difficult, labor-intensive process, and (2) that use of such pattern-monitoring must provide rapid turn-around or response time. We present a novel data structure, called extended action graph, and associated algorithms to overcome these drawbacks. At its core, our technique relies on effectively identifying and caching specifications from (correct/faulty) patterns learned via machine-learning algorithm. We describe the design and implementation of our technique and show its practical applicability in the domain of security monitoring of sendmail software

Peer-to-peer (P2P) video system is characterized by two features: 1) a video is usually available on many participating hosts, and 2) different hosts typically have different sets of videos, though some may partially overlap. From a client's perspective, it can be served by any host having the video it requests. From a server's perspective, it will be used to serve any client requesting the videos it has. In this paper, we refer to the problem of who-serves-whom as service scheduling and show that different matches between clients and servers can result in significantly different system performance. Finding a right server for each client is challenging not only because a client can choose only the servers that are within its limited search scope, but also because clients arrive at different times, which are not known a priori. To address these challenges, we propose a novel technique called shaking and our performance study shows that it can effectively boost the overall system performance.

Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. We present a new computer-based method that allows automated measurement of a number of metrics that likely reflect the quality of the colonoscopic procedure. The method is based on analysis of a digitized video file created during colonoscopy, and produces information regarding insertion time, withdrawal time, images at the time of maximal intubation, the time and ratio of clear versus blurred or non-informative images, and a first estimate of effort performed by the endoscopist. As these metrics can be obtained automatically, our method allows future quality control in the day-to-day medical practice setting on a large scale. In addition, our method can be adapted to other healthcare procedures. Last but not least, our method may be useful to assess progress during colonoscopy training, or as part of endoscopic skills assessment evaluations.

Core-based routing with quality of service (QoS) support is essential in facilitating multi-sender multimedia multicast applications such as video conferencing and virtual collaboration applications. In this paper, we introduce a new distributed core selection protocol that has the following desirable properties. First, the protocol utilizes a new distributed primary core selection algorithm that selects as many primary cores per multicast group as necessary, to maximize the number of group members with satisfied QoS requirements. Second, the protocol is distributed, preventing a single router from becoming a hot spot and a single point of failure during core selection. Lastly, the protocol employs a distributed backup core selection algorithm to provide quick recovery should some primary cores fail. Our analytical experiments show that the proposed protocol significantly satisfies more group members with noticeably less communication overhead than a recent core selection algorithm with QoS support using a single core.

To provide scalable video-on-demand services, precious system bandwidth must be shared among video requests. Although many efficient bandwidth-sharing techniques have been proposed, they are all designed for homogeneous receivers, i.e., the clients are assumed to have the same receiving bandwidth. For networks with client heterogeneity, these techniques either cannot work or have to compromise their performance. We address this problem and propose an efficient solution allowing each client to use its whole receiving bandwidth to download data. To serve a client, the server first selects a set of serving channels for the client according to its actual receiving bandwidth. The video data needed for this client are then scheduled and dynamically adjusted for delivery over these channels. We evaluate the performance of the new technique using simulation and compare it with an existing scheme. Our study shows that by effectively leveraging client heterogeneity, the new technique achieves significantly smaller service latency.

Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable for endoscopic research and education. The first necessary step for the analysis is parsing for semantic units. Since the characteristics of colonoscopy videos differ from those of videos studied in the literature, we introduce a new video parsing framework that includes: (i) a new scene definition and a new video parsing paradigm; (ii) a novel scene segmentation algorithm using audio analysis and finite state automata to recognize scenes and associated boundaries. Our experimental results show average precision and recall of 95% and 81%, respectively, for parsing scenes. The framework is extensible to videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.

Periodic broadcast is a cost-effective solution for disseminating popular videos. This strategy has the potential to serve a very large community with minimal broadcast bandwidth: regardless of the number of video requests, the worst service latency to all clients is constant. Although many efficient schemes have been proposed, most of them impose some rigid requirement on client receiving bandwidth. They either demand clients to have the same bandwidth as the video server, or limit them to receive no more than two video streams at any one time. In our previous work, we addressed this problem by proposing a client-centric approach (CCA). Unlike any other technique, CCA takes both server broadcast bandwidth and client receiving bandwidth into design consideration. More specifically, CCA allows clients to use all their receiving capability for prefetching broadcast data. Therefore, given a fixed broadcast bandwidth, CCA can achieve shorter broadcast period with an improved client communication capability. In this paper, we present a novel technique to further leverage client bandwidth for more efficient video broadcast. We prove the correctness of this new technique and provide analytical evaluations to show that with the same client bandwidth, it achieves significantly better performance than CCA.

Colonoscopy is an important screening tool for colorectal cancer. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. The video data are displayed on a monitor for real-time analysis by the endoscopist. We call videos captured from colonoscopic procedures colonoscopy videos. Because these videos possess unique characteristics, new types of semantic units and parsing techniques are required. In this paper, we define new semantic units called operation shots, each is a segment of visual and audio data that correspond to a therapeutic or biopsy operation. We introduce a new spatio-temporal analysis technique to detect operation shots. Our experiments on colonoscopy videos demonstrate that the technique does not miss any meaningful operation shots and incurs a small number of false operation shots. Our prototype parsing software implements the operation shot detection technique along with our other techniques previously developed for colonoscopy videos. Our browsing tool enables users to quickly locate operation shots of interest. The proposed technique and software are useful (1) for post-procedure reviews and analyses for causes of complications due to biopsy or therapeutic operations, (2) for developing an effective content-based retrieval system for colonoscopy videos to facilitate endoscopic research and education, and (3) for development of a systematic approach to assess endoscopists' procedural skills.

This paper describes the design and implementation of MPRES, a Multimedia Presentation Toolkit for the WWW. The WWW has seen phenomenal growth over the last couple of years. It has become a vast repository of multimedia information that is accessible to virtually anyone having a browser. MPRES is a multimedia presentation system that allows a user to compose and render a presentation consisting of objects referenced by their URLs (Uniform Resource Locators). It uses the concept of dynamic documents to render on a WWW browser, a sequence of multimedia scenarios, having objects of types such as audio, image, plaintext, HTML (Hypertext Markup Language) document and animation. MPRES Author, the authoring subsystem, allows the user to interactively test and compose such a presentation, using the Netscape Navigator to collect multimedia resources from the WWW. A presentation database stores the presentations and provides a convenient frontend for accessing them. © 1997 John Wiley & Sons,Ltd.

Several information protocols exist for exchange of multimedia information. The {H}yper{T}ext {T}ransfer {P}rotocol (HTTP) of NCSA combines hypertext techniques with multimedia to provide an intuitive, easy-to-use hypermedia interface. However, HTTP does not provide the means to synchronize different multimedia channels. The NCSA hypermedia browser, Mosaic, has been modified to incorporate synchronization constraints. Platform-independent multimedia authoring programs have been combined in a new synchronization scheme. The {H}yper{T}ext {M}arkup {L}anguage (HTML) has been extended to allow the specification of temporal dependencies between different media channels. This extension allows synchronization in the authoring of hypermedia presentations using Mosaic.

Recent advances in media processing technology have resulted in the generation of vast amounts of multimedia information. There exists a need to manage this information. This paper describes the design and implementation of an object-oriented system used for remote multimedia database access. The work includes the design of a framework to model multimedia information in the object-oriented database POET, the development of a POET multimedia server to provide access to this information and the design of client interfaces to the system.

Abstract With more and more computer users having access to the Internet, it is important for them to access the vast amount of information in an efficient and easy manner. This project explores one of the possibilities for providing an integrated system to the user to access the different types of media like text, image, audio and video stored in different databases on remote machines without the user having to know about the underlying mechanism involved in accessing the database. Such a system must hide the location of the remote data, retrieve the information, process the results and present it to the user. We have used the National Center for Supercomputer Applications (NCSA) Mosaic's Gateway Structured Query Language (GSQL), which enables a system developer to build input forms, query to the remote database in a relatively easy manner and present the results to the user. With the help of this prototype, the development time to build such an application is considerably reduced as compared with developing the same application using the existing Graphical User Interface (GUI) tools like the X‐Window system.

Abstract This paper describes the design, implementation and testing of a set of software modules that are used for remote database access in a distributed computing environment. The goal of our research and development is to implement a client server model using Structured Query Language (SQL) functions in the Open Software Foundation's (OSF) distributed computing environment (DCE). This design is compared with another which simply uses the sockets application programming interface (API), running over the transmission control protocol/internet protocol (TCP/IP), and is implemented in subroutines that act similar to the remote procedure call (RPC) generated stub code. The prototypes for the remote SQL access project are implemented using an IBM RISC System/6000 (the client) running the AIX operating system and an IBM AS/400 (the server) running the OS/400 operating system. We selected the AS/400 for its database abilities, and the RISC System/6000 since the DCE software is available for it.

The objective of the study was to achieve balanced load among processors, reduce the communication overhead of the load balancing algorithm, and improve respource utilization, which results in better average resonse time. A communication protocol and a fully distributed algorithm for dynamic load balancing through task migration in a connected N-processor network are presented. Each processor communicates its load directly with only a subset (of the size square root N) of processors, reducing communication traffic and average response time. It is proved that the given algorithm will perform task migration even if there is only one light load processor and one heavy load processor in the system. Simulation results show that the proposed scheme can save up to 60% of the protocol messages used by the broadcast algorithms and can reduce the average response time.>

A mechanism for modeling timing, precedence, and data-consistency constraints on concurrently executing processes is presented. The model allows durations and intervals between events to be specified. An algorithm is provided to detect schedules which may be unsafe with respect to the constraints. This work, motivated by the design and validation of autonomous error-recovery strategies on the Galileo spacecraft, appears to be applicable to a variety of asynchronous real-time systems.>

No abstract available.

A multilevel relational database system handles data at different security classifications and provides access to users with different security clearances. Many methods for enforcing security in this environment have been investigated. This paper presents a unified approach to multilevel database security based on two ideas: a trusted filter and an inference engine. These two approaches are introduced separately and then the motivation for the unified system and the system model itself are presented.