Online Framework Research Articles

A Cyber-Physical Modeling and Assessment Framework for Power Grid Infrastructures

The integration of cyber communications and control systems into the power grid infrastructure is widespread and has a profound impact on the operation, reliability, and efficiency of the grid. Cyber technologies allow for efficient management of the power system, but they may contain vulnerabilities that need to be managed. One important possible consequence is the introduction of cyber-induced or cyber-enabled disruptions of physical components. In this paper, we propose an online framework for assessing the operational reliability impacts due to threats to the cyber infrastructure. This framework is an important step toward addressing the critical challenge of understanding and analyzing complex cyber-physical systems at scale.

Application Domains for a Location-based Mobile Application Creator

In recent years, the use of mobile applications and computer-based games as pedagogical tools has become more and more popular. Additionally, many mobile devices enhance the user experience by providing different sensors, e.g. to deliver location-based educative information. In this article we introduce an online tool for easily creating location-based mobile applications that combine teaching and gaming activities to make users aware of their current environment or situation. First, we briefly present the Location-based Mobile Application Creator (LMAC), an online framework for creating such mobile solutions and discuss potential application scenarios. Then, we introduce a first mobile application for educational purposes created with LMAC in more detail: a game-based scavenger hunt for children enabling them to learn facts about the city of Graz, Austria. Similar to a traditional scavenger hunt, users are able to gather geo-referenced information with mobile devices (smartphones and tablets) and learn about the environment in a playful way. With LMAC, teachers can easily create individual scavenger hunts adapted to the current subject matter. This paper is an extended version of our work published in 2014 at the IMCL-conference.

High-Performance Video Condensation System

Video synopsis or condensation is a smart solution for fast video browsing and storage. However, most of the existing methods work offline, where two main phases are required. The first phase is to prepare tubes and background images. The second phase is to rearrange tubes and stitch them into backgrounds. However, with a long video sequence, the first phase is memory consuming for data storage, and the second phase is computationally expensive to rearrange all tubes simultaneously. To overcome these problems, we propose a high-performance video condensation system based on an online content-aware framework. The online framework transforms the optimization problem of tube rearrangement into a stepwise optimization problem. Therefore, it can condense video with much less memory and higher speed than the offline framework. With the aid of this transformation, the proposed system can process input videos and produce condensed videos simultaneously. Thus it is suitable for real-time endless surveillance videos. Meanwhile, the online mechanism allows users to directly visit the condensation video that has been generated. Moreover, the content-aware mechanism makes the proposed system able to automatically determine the duration of a condensed video. Finally, the proposed system uses Graphic Processing Unit (GPU) and multicore techniques to improve the speed. Extensive experiments that validate the high efficiency of the system are presented.

Video-based road detection via online structural learning

Video-based road detection is a crucial enabler for the successful development of driver assistant and robot navigation systems. But reliable detection is still on its infancy and deserves further research. In order to adapt to the situation consisting of environmental varieties, an online framework is proposed focusing on exploring the structure cue of the feature vectors. Through the structural support vector machine, the road boundary and non-boundary instances are firstly discriminated. Then they are utilized to fit a complete road boundary. After that, the road region is accordingly inferred and the obtained results are treated as ground truth to update the learned model. Three contributions are claimed in this work: online-learning updating, structural information consideration, and targeted sampling selection. The proposed method is finally evaluated on several challenging videos captured by ourselves. Qualitative and quantitative results show that it outperforms the other competitors.

Mining the Situation: Spatiotemporal Traffic Prediction With Big Data

With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions.

SU-D-BRD-05: Online Framework for Plan Tracking and Automatic Checks in Radiation Therapy

Purpose: To develop an online framework that tracks a patient’s plan from initial simulation to treatment and that helps automate elements of the physics plan checks usually performed in the record and verify (RV) system and treatment planning system. Methods: We have developed PlanTracker, an online plan tracking system that automatically imports new patients tasks and follows it through treatment planning, physics checks, therapy check, and chart rounds. A survey was designed to collect information about the amount of time spent by medical physicists in non-physics related tasks. We then assessed these non-physics tasks for automation. Using these surveys, we directed our PlanTracker software development towards the automation of intra-plan physics review. We then conducted a systematic evaluation of PlanTracker’s accuracy by generating test plans in the RV system software designed to mimic real plans, in order to test its efficacy in catching errors both real and theoretical. Results: PlanTracker has proven to be an effective improvement to the clinical workflow in a radiotherapy clinic. We present data indicating that roughly 1/3 of the physics plan check can be automated, and the workflow optimized, and show the functionality of PlanTracker. When the full system is in clinical use we willmore » present data on improvement of time use in comparison to survey data prior to PlanTracker implementation. Conclusion: We have developed a framework for plan tracking and automatic checks in radiation therapy. We anticipate using PlanTracker as a basis for further development in clinical/research software. We hope that by eliminating the most simple and time consuming checks, medical physicists may be able to spend their time on plan quality and other physics tasks rather than in arithmetic and logic checks. We see this development as part of a broader initiative to advance the clinical/research informatics infrastructure surrounding the radiotherapy clinic. This research project has been financially supported by Varian Medical Systems, Palo Alto, CA, through a Varian MRA.« less

The struggle for PSB 2.0: An assessment

Abstract A vigorous debate about the redefinition and expansion of Public Service Broadcasting (PSB) in the new media environment has taken centre stage in global policy fora in the last two decades. It is now crucial to develop new conceptualizations of PSB and analyse how the public service philosophy is being adjusted to the new online and multi-platform environment. Born and Prosser identified the principles of citizenship, universality and quality as the normative criteria behind PSB in a broadcasting environment. This construction offers a valid starting point for the development of a new normative framework for PSB online that I have called PSB 2.0. By drawing on documentary and policy analysis of different European case studies (United Kingdom, Spain, Italy, Denmark and France) this article aims to establish a new framework of PSB that can be applied to the online world. Thus, the study identifies the principles of citizenship, universality, quality and trust as the pillars of PSB 2.0.

Achieving High Performance With TCP Over 40 GbE on NUMA Architectures for CMS Data Acquisition

TCP and the socket abstraction have barely changed over the last two decades, but at the network layer there has been a giant leap from a few megabits to 100 gigabits in bandwidth. At the same time, CPU architectures have evolved into the multi-core era and applications are expected to make full use of all available resources. Applications in the data acquisition domain based on the standard socket library running in a Non-Uniform Memory Access (NUMA) architecture are unable to reach full efficiency and scalability without the software being adequately aware about the IRQ (Interrupt Request), CPU and memory affinities. During the first long shutdown of LHC, the CMS DAQ system is going to be upgraded for operation from 2015 onwards and a new software component has been designed and developed in the CMS online framework for transferring data with sockets. This software attempts to wrap the low-level socket library to ease higher-level programming with an API based on an asynchronous event driven model similar to the DAT uDAPL API. It is an event-based application with NUMA optimizations, that allows for a high throughput of data across a large distributed system. This paper describes the architecture, the technologies involved and the performance measurements of the software in the context of the CMS distributed event building.

Approximate policy iteration using regularised Bellman residuals minimisation

In this paper we present an approximate policy iteration (API) method called API‐BRMϵ using a very effective implementation of incremental support vector regression (SVR) to approximate the value function able to generalise in continuous (or large) space reinforcement learning (RL) problems. RL is a methodology able to solve complex and uncertain decision problems usually modelled as Markov decision problems. API-BRMϵ is formalised as a non-parametric regularisation problem based on an outcome of the Bellman residual minimisation (BRM) which is able to minimise the variance of the problem. API-BRMϵ is incremental and can be applied to RL using the on-line agent interaction framework. Based on non-parametric SVR API-BRMϵ is able to find the global solution of the problem with convergence guarantees to the optimal solution. A value function should be defined to find the optimal policy specifying the total reward that an agent might expect in its current state taking one action. Therefore, the agent will use the value function to choose the action to take. Some experimental evidence and performance for well-known RL benchmarks are presented.

WKGGSeq: A Comprehensive Strategy-Based and Disease-Targeted Online Framework to Facilitate Exome Sequencing Studies of Inherited Disorders.

With the rapid advances in high-throughput sequencing technologies, exome sequencing and targeted region sequencing have become routine approaches for identifying mutations of inherited disorders in both genetics research and molecular diagnosis. There is an imminent need for comprehensive and easy-to-use downstream analysis tools to isolate causal mutations in exome sequencing studies. We have developed a user-friendly online framework, wKGGSeq, to provide systematic annotation, filtration, prioritization, and visualization functions for characterizing causal mutation(s) in exome sequencing studies of inherited disorders. wKGGSeq provides: (1) a novel strategy-based procedure for downstream analysis of a large amount of exome sequencing data and (2) a disease-targeted analysis procedure to facilitate clinical diagnosis of well-studied genetic diseases. In addition, it is also equipped with abundant online annotation functions for sequence variants. We demonstrate that wKGGSeq either outperforms or is comparable to two popular tools in several real exome sequencing samples. This tool will greatly facilitate the downstream analysis of exome sequencing data and can play a useful role for researchers and clinicians in identifying causal mutations of inherited disorders. The wKGGSeq is freely available at http://statgenpro.psychiatry.hku.hk/wkggseq or http://jjwanglab.org/wkggseq, and will be updated frequently.

Buildng the ISRPM disaster acute rehabilitation team program

Program/Project Purpose: Rehabilitation response following a natural disaster has been progressively documented as an essential strategy for recovery and relief. In order to respond quickly, rehabilitation professionals, policy makers, relief volunteers and health officials need adequate training. The International Society for Physical and Rehabilitation Medicine arranged a directive to improve rehabilitation after disasters by creating a training program built off of the first hand documentation of past disasters around the world. Structure/Method/Design: After putting together a preliminary online framework for the Disaster Acute Rehabilitation Team curriculum, three University of Michigan students travelled to China, Chile and Taiwan individually to conduct interviews with disaster victims, doctors, health officials, nurses, volunteers and hospital administration in post-disaster areas to assess their personal postdisaster rehabilitation experiences. Dozens of interviews were conducted and over 20 hours of video and over 2,000 photographs were collected. The student/physician team consolidated this information in the form of training videos for rehabiliation physicians, online quizzes, tutorials, disaster photography, and realistic disaster scenarios. We organized all of this information into a framework for initial trials as a PowerPoint presentation. Outcomes & Evaluation: The framework for the core curriculum consists of 3 modules: a module for training rehabilitation professionals who will respond immediately to a natural disaster, another module for training rehabilitation professionals who will be team leaders and a third for rehabilitation professionals who will coordinate with national emergency planning organizations and NGO’s. All of the modules have been successfully completed and edited by international disaster experts from the International Society for Physical Medicine and Rehabilitation. The next step consists of making the modules internet friendly and inviting physicians to become trained in disaster rehabilitation. Going Forward: It is imperative that the rehabilitation response be rapid and efficient to improve survival rate following natural disaster. The DART curriculum provides a method for rehabilitation professionals who are involved in disaster work to plan and respond more effectively. The first-hand experience gained through interviews with rehabilitation professionals and tours of the disaster sites allowed for the production of a core curriculum and the preemptive planning for acute rehabilitation response during a natural disaster. The next challenges includes finalizing our curriculum into website format and recruiting rehabilitation physicians from around the world to become trained in Natural Disaster Response. Funding: No Funding Source As of Now. Abstract #: 01ETC096

Community health workers understanding and attitudes regarding intimate partner violence in the dominican republic

Program/Project Purpose: Rehabilitation response following a natural disaster has been progressively documented as an essential strategy for recovery and relief. In order to respond quickly, rehabilitation professionals, policy makers, relief volunteers and health officials need adequate training. The International Society for Physical and Rehabilitation Medicine arranged a directive to improve rehabilitation after disasters by creating a training program built off of the first hand documentation of past disasters around the world. Structure/Method/Design: After putting together a preliminary online framework for the Disaster Acute Rehabilitation Team curriculum, three University of Michigan students travelled to China, Chile and Taiwan individually to conduct interviews with disaster victims, doctors, health officials, nurses, volunteers and hospital administration in post-disaster areas to assess their personal postdisaster rehabilitation experiences. Dozens of interviews were conducted and over 20 hours of video and over 2,000 photographs were collected. The student/physician team consolidated this information in the form of training videos for rehabiliation physicians, online quizzes, tutorials, disaster photography, and realistic disaster scenarios. We organized all of this information into a framework for initial trials as a PowerPoint presentation. Outcomes & Evaluation: The framework for the core curriculum consists of 3 modules: a module for training rehabilitation professionals who will respond immediately to a natural disaster, another module for training rehabilitation professionals who will be team leaders and a third for rehabilitation professionals who will coordinate with national emergency planning organizations and NGO’s. All of the modules have been successfully completed and edited by international disaster experts from the International Society for Physical Medicine and Rehabilitation. The next step consists of making the modules internet friendly and inviting physicians to become trained in disaster rehabilitation. Going Forward: It is imperative that the rehabilitation response be rapid and efficient to improve survival rate following natural disaster. The DART curriculum provides a method for rehabilitation professionals who are involved in disaster work to plan and respond more effectively. The first-hand experience gained through interviews with rehabilitation professionals and tours of the disaster sites allowed for the production of a core curriculum and the preemptive planning for acute rehabilitation response during a natural disaster. The next challenges includes finalizing our curriculum into website format and recruiting rehabilitation physicians from around the world to become trained in Natural Disaster Response. Funding: No Funding Source As of Now. Abstract #: 01ETC096

Adaptive Cross-Network Cross-Layer Design in Heterogeneous Wireless Networks

A cross-network cross-layer design method is proposed to exploit the trunking, diversity, and best service assignment gains available in a heterogeneous wireless network (HWN), consisting of orthogonal radio access networks (RANs) and interference-limited RANs. Accounting for traffic-level dynamics and channel fading, we jointly design the distribution strategy for elastic and inelastic traffic, and the radio resource management strategy for RANs, in a network-separable control architecture. Optimal and quantified near-optimal radio allocation schemes are proposed for each type of RAN, which are combined into an on-line design framework that over time provides asymptotically optimal performance, maximizing the sum throughput utility for elastic traffic while guaranteeing the throughput requirements of inelastic traffic. Extensive simulation results demonstrate substantial performance improvement against suboptimal alternatives.

Can you learn to lawyer online? A blended learning environment case study

Law students’ future clients and employers, and the broader community, all deserve graduates to be equipped with not only substantive legal knowledge, but also a range of skills and practical knowledge. However, most law schools face resource pressures that mean that traditional skills development methodologies (which are often resource intensive) can only be used judiciously. In this resource-poor environment, skills development methodologies which incorporate new technologies can be one way to assist law students to develop the professional skills they require. Online learning tools have the potential to be resource friendly, and law schools may therefore be able to utilise them to ensure that maximum learning potential is achieved from the limited resources available. Considering an online or blended skills development framework is also supported by evidence that new technology learning tools can usefully contribute to skills development. In this article the potential for online learning to replicate aspects of an apprenticeship model of learning is explored, as a means of explaining this contribution. This analysis is intended to facilitate consideration of a broad panoply of learning tools for skills development, and inform educators considering adding a new technology component to student skills development.

Recursive On-Line <formula formulatype="inline"> <tex Notation="TeX">${(2{\rm D})}^2{\rm PCA}$</tex></formula> and Its Application to Long-Term Background Subtraction

In this paper, we propose a novel background subtraction method which enables reliable detection of foreground objects in a long surveillance video stream. Recently, although much progress has been made in the field of background subtraction, there are still challenging scenarios (e.g., high frequency motion of dynamic texture, non-stationary motion of camera, abrupt changes of illumination, etc.) in the long surveillance videos in which even state-of-the-art methods are often prone to fail. To cope with these challenging scenarios effectively, in the proposed method, a background model is initialized in a low-dimensional subspace and then updated periodically based on a novel recursive on-line ${(2{\rm D})}^2{\rm PCA}$ algorithm developed in this paper. Moreover, a threshold map is also updated in a scene-adaptive manner for labeling each pixel in a scene either foreground or background independently. Based on this on-line framework, the background of a surveillance video stream is reconstructed over time, thereby facilitating the detection of foreground objects reliably. In extensive experiments, we demonstrate that the proposed background subtraction method can cope with the aforementioned challenging scenarios more favorably than the state-of-the-art methods.

Benchmarks for Characterization of Minima, Transition States, and Pathways in Atomic, Molecular, and Condensed Matter Systems.

A set of benchmark systems is defined to compare different computational approaches for characterizing local minima, transition states, and pathways in atomic, molecular, and condensed matter systems. Comparisons between several commonly used methods are presented. The strengths and weaknesses are discussed, as well as implementation details that are important for achieving good performance. All of the benchmarks and methods are provided in an online database to make the implementation details available and the results reproducible. While this paper provides a snapshot of the benchmark results, the online framework is structured to be dynamic and incorporate new methods and codes as they are developed.

A flexible framework for online document segmentation by pairwise stroke distance learning

A flexible framework for handling a variety of segmentation problems in online handwritten documents is introduced. The strategy relies on single linkage clustering and a pairwise stroke distance that is globally trained for direct optimization of the segmentation. We define a variety of features that can contribute to the pairwise distance definition and show how to select a good combination of features for dealing with a new online handwritten document segmentation task. Our experiments demonstrate the validity of the method on a large range of segmentation tasks over several types of documents from various publicly available databases.

An Online Performance Prediction Framework for Service-Oriented Systems

The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance prediction framework, called OPred, to provide personalized service-oriented system performance prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.

Optimal allocation of digital marketing budget: The empirical Bayes approach

The article outlines a framework for online advertising budget allocation. First, it explores the empirical Bayes methodology for learning the effectiveness of different online ad placements – from historical data of varying quality. Second, it describes an analytical procedure for optimal budget allocation, which builds on risk management and reinforcement learning techniques.

Progressive 3D shape segmentation using online learning

In this article, we propose a progressive 3D shape segmentation method, which allows users to guide the segmentation with their interactions, and does segmentation gradually driven by their intents. More precisely, we establish an online framework for interactive 3D shape segmentation, without any boring collection preparation or training stages. That is, users can collect the 3D shapes while segment them, and the segmentation will become more and more precise as the accumulation of the shapes.Our framework uses Online Multi-Class LPBoost (OMCLP) to train/update a segmentation model progressively, which includes several Online Random forests (ORFs) as the weak learners. Then, it performs graph cuts optimization to segment the 3D shape by using the trained/updated segmentation model as the optimal data term. There exist three features of our framework. Firstly, the segmentation model can be trained gradually during the collection of the shapes. Secondly, the segmentation results can be refined progressively until users’ requirements are met. Thirdly, the segmentation model can be updated incrementally without retraining all shapes when users add new shapes. Experimental results demonstrate the effectiveness of our approach.