Concurrent Users Research Articles

The next-generation in-stadium experience (keynote)

YinzCam is a cloud-hosted service that provides sports fans with real-time scores, news, photos, statistics, live radio, streaming video, etc., on their mobile devices. YinzCam’s infrastructure is currently hosted on Amazon Web Services (AWS) and supports over 30 million installs of the official mobile apps of 140+ NHL/NFL/NBA/NRL/NCAA sports teams and venues. YinzCam’s workload is necessarily multi-modal (e.g., pre-game, in-game, post-game, game-day, non-gameday), with normal game-time traffic being twenty-fold of that on non-game days. This paper describes the evolution of YinzCam’s production architecture and distributed infrastructure, from its beginnings in 2009, when it was used to support thousands of concurrent users, to today’s system that supports millions of concurrent users on any game day. We also discuss key new opportunities to improve the fan experience inside the stadium of the future, without impacting the available bandwidth, by crowd-sourcing the thousands of mobile devices that are in fans’ hands inside these venues. We present Krowd, a novel distributed key-value store for promoting efficient content sharing, discovery and retrieval across the mobile devices inside a stadium. We present CHIPS, a system that ensures that users’ privacy is maintained while their devices participate in the crowdsourced infrastructure.

Prevalence and Trends of Concurrent Opioid Analgesic and Benzodiazepine Use Among Veterans Affairs Patients with Post-traumatic Stress Disorder, 2003-2011.

Patients with post-traumatic stress disorder (PTSD) have complex and multiple symptoms, including anxiety, insomnia, and co-occurring pain, often treated with opioids and benzodiazepines. While concurrent use of these medications poses safety concerns, little is known about the trends of long-term concurrent use and the prevalence of high-risk conditions among those who are prescribed them. Study objectives were to examine the trends in annual prevalence of long-term concurrent opioid and benzodiazepine use among patients with PTSD and prevalence of high-risk conditions in concurrent users of these medications. Retrospective review of pharmacy records of the Veteran Affairs Northwest Integrated Network (VISN20). Patients (n = 66,210) with PTSD receiving care during 2003-2011. Concurrent use was defined as overlapping opioid and benzodiazepine prescriptions for ≥90 consecutive days. Gender-specific logistic regressions estimated long-term concurrent use of these medications and tested for linear trends over 9-years. The trend in age-adjusted long-term concurrent opioid and benzodiazepine use over 9-years increased 52.7%, from 3.6% (95% confidence interval, 3.3-3.9%) to 5.5% (5.3-5.8%), in men and 79.5%, from 3.9% (3.0-5.0%) to 7.0% (6.2-7.9%), in women. In 2011, 17.1% of long-term concurrent users were prescribed morphine-equivalent daily doses of opioids ≥100 mg and 49.4% had a documented high-risk condition. Despite known risks associated with prescribing opioids and benzodiazepines concurrently, the adjusted prevalence of long-term concurrent use rose significantly among men and women with PTSD in VISN20 over a 9-year period. Common use of these medications among patients with high-risk conditions suggests comprehensive strategies are needed to identify and monitor patients at increased risk for adverse outcomes.

STREETGEN: IN-BASE PROCEDURAL-BASED ROAD GENERATION

Abstract. Streets are large, diverse, and used for conflicting transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge because of the diversity, size, and scale of the details (~ cm for cornerstone) required. The state-of-the-art focuses on roads and is strongly oriented by each application (simulation, visualisation, planning). We propose a unified framework that works on real Geographic Information System (GIS) data and uses a strong, yet simple hypothesis when possible to produce coherent street modelling at the city scale or street scale. Because it is updated only locally in subsequent computing, the result can be improved by adapting input data and the parameters of the model. We reconstruct the entire Paris streets in a few minutes and show how the results can be edited simultaneously by several concurrent users.

An efficient image aesthetic analysis system using Hadoop

Assessing aesthetic appeal of images is a highly subjective task and has attracted a lot of research interests recently. Prior researchers have developed several aesthetic analysis systems on standalone computers. However, it is challenging to run the algorithms on mobile devices since the process of aesthetic analysis is quite complicated and time-consuming, especially for large amount of images. Hadoop is a popular technology for big data processing on cloud to offload computing burden from terminals. However it has NOT been used on image aesthetic yet. In this paper, we present an image aesthetic analysis system based on Hadoop framework to provide an efficiency solution and better user experience. We address several major problems: (1) adapt MapReduce for image data format and aesthetic analysis algorithms; (2) improve computing performance for large amount of small image files; (3) design a dynamic scheduling mechanism to optimize concurrent multiple users׳ requests; (4) design an effective commutation service between cloud and terminals. Experimental results demonstrate significant performance improvements with our system. At the same time, the system efficiency increases linearly with the expansion of the slaves in Hadoop.

Risk factors for concurrent use of benzodiazepines and opioids among individuals under community corrections supervision

BackgroundThe use of heroin and prescription opioids has increased over the past decade. The concurrent use of opioids with other depressants such as benzodiazepines increases the risk of overdose death compared with use of either drug alone. This study examined factors associated with concurrent use of opioids and benzodiazepines in a criminal justice sample in the state of Alabama. MethodsThe Addiction Severity Index (ASI) and urine drug screen results from 28,570 individuals who were under community corrections supervision from 2002–2012 were examined for independent or concurrent opioid and benzodiazepine use. Multinomial logistic regression analyses were conducted to determine associations between socio-demographic characteristics and drug use. ResultsConcurrent use was detected in 11.5% of the sample. Concurrent use of opioids and benzodiazepines or use of either drug alone was associated with being White, female, married, prescribed psychiatric medications, having seen a physician in the past two years, cannabis use, and having a drug-related offense. Concurrent users were more likely to be unemployed or disabled and have received counseling, and less likely to have completed college, live with relatives or friends, have a history of hallucinations, or have an offense against a person relative to nonusers. DiscussionWhile significant overlap of risk factors exists between individuals with concurrent use versus sole use of opioids or benzodiazepines, individuals with concurrent use generally have more social dysfunction than individuals who tested for either substance alone. Concurrent users may need more psychosocial resources and intensive treatments to promote recovery.

Guoguo: Enabling Fine-Grained Smartphone Localization via Acoustic Anchors

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

The Cameraman Operating My Virtual Camera is Artificial

In this article, we argue that the energy spent in designing autonomous camera control systems is not spent in vain. We present a real-time virtual camera system that can create smooth camera motion. Similar systems are frequently benchmarked with the human operator as the best possible reference; however, we avoid a priori assumptions in our evaluations. Our main question is simply whether we can design algorithms to steer a virtual camera that can compete with the user experience for recordings from an expert operator with several years of experience? In this respect, we present two low-complexity servoing methods that are explored in two user studies. The results from the user studies give a promising answer to the question pursued. Furthermore, all components of the system meet the real-time requirements on commodity hardware. The growing capabilities of both hardware and network in mobile devices give us hope that this system can be deployed to mobile users in the near future. Moreover, the design of the presented system takes into account that services to concurrent users must be supported.

다중 CPU 서버 환경에서 동시 사용자를 위한 부하조절 기반 볼륨 가시화 시스템

This research suggests a load balancing method for a volume rendering system which supports concurrent users. When concurrent users use a volume rendering server system, the computational resources are occupied by a particular user by turns because each process consumes the computational resources as much as possible. In this case, the previous method shows acceptable throughput but the latency is increased for each user. In this research, we suggest a method to improve the latency without performance degradation. Each process makes concessions for taking the resources according to the number of users connected to the system. And we propose a load balancing method in the dynamic situation in which the number of users can vary. Using our methods, we can improve the latency time for each user.

Simultaneous versus concurrent use of alcohol and cannabis in the National Alcohol Survey.

Cannabis is the most commonly used drug among those who drink, yet no study has directly compared those who use cannabis and alcohol simultaneously versus concurrently (i.e., separately) in the adult general population. Here, we assess differences in demographics, alcohol-related social consequences, harms to self, and drunk driving across simultaneous, concurrent, and alcohol-only using groups. Secondary analyses of the 2005 and 2010 National Alcohol Survey (N=8,626; 4,522 female, 4,104 male), a Computer Assisted Telephone Interview survey of individuals aged 18 and older from all 50 states and DC. Blacks and Hispanics are over-sampled. Data were collected using list-assisted Random Digit Dialing. Multinomial and multivariable logistic regressions were used for analyses. The prevalence of simultaneous use was almost twice as high as concurrent use, implying that individuals who use both cannabis and alcohol tend to use them at the same time. Furthermore, simultaneous use was associated with increased frequency and quantity of alcohol use. Simultaneous use was also the most detrimental: compared to alcohol only, simultaneous use approximately doubled the odds of drunk driving, social consequences, and harms to self. The magnitudes of differences in problems remained when comparing drunk driving among simultaneous users to concurrent users. The overall set of results is particularly important to bear in mind when studying and/or treating problems among alcohol/cannabis co-users because they demonstrate that in the general population, co-users are a heterogeneous group who experience different likelihoods of problems relative to co-use patterns.

Web based visualization of large climate data sets

We have implemented the USGS National Climate Change Viewer (NCCV), which is an easy-to-use web application that displays future projections from global climate models over the United States at the state, county and watershed scales. We incorporate the NASA NEX-DCP30 statistically downscaled temperature and precipitation for 30 global climate models being used in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), and hydrologic variables we simulated using a simple water-balance model. Our application summarizes very large, complex data sets at scales relevant to resource managers and citizens and makes climate-change projection information accessible to users of varying skill levels. Tens of terabytes of high-resolution climate and water-balance data are distilled to compact binary format summary files that are used in the application. To alleviate slow response times under high loads, we developed a map caching technique that reduces the time it takes to generate maps by several orders of magnitude. The reduced access time scales to >500 concurrent users. We provide code examples that demonstrate key aspects of data processing, data exporting/importing and the caching technique used in the NCCV.

An Efficient Parallel Map Visualization Framework for Large Vector Data

With the tremendous development of surveying and mapping technologies, the volume of vector data is becoming larger. For mapping workers and other GIS scientists, map visualization is one of the most common functions of GIS software. But it is also a time-consuming process when processing massive amounts of vector data. Especially in an Internet map service environment, large numbers of concurrent users can cause major processing delays. In order to address this issue, this paper develops an efficient parallel visualization framework for large vector data sets by leveraging the advantages and characteristics of graphics cards, focusing on storage strategy and transfer strategy. The test results demonstrate that this new approach can reduce the computing times for visualizing large vector maps.

Enhancing the management of unstructured data in e-learning systems using MongoDB

Purpose – Current e-learning platforms are based on relational database management systems (RDBMS) and are well suited for handling structured data. However, it is expected from e-learning solutions to efficiently handle unstructured data as well. The purpose of this paper is to show an alternative to current solutions for unstructured data management. Design/methodology/approach – Current repository-based solution for file management was compared to MongoDB architecture according to their functionalities and characteristics. This included several categories: data integrity, hardware acquisition, processing files, availability, handling concurrent users, partition tolerance, disaster recovery, backup policies and scalability. Findings – This paper shows that it is possible to improve e-learning platform capabilities by implementing a hybrid database architecture that incorporates RDBMS for handling structured data and MongoDB database system for handling unstructured data. Research limitations/implications – The study shows an acceptable adoption of MongoDB inside a service-oriented architecture (SOA) for enhancing e-learning solutions. Practical implications – This research enables an efficient file handling not only for e-learning systems, but also for any system where file handling is needed. Originality/value – It is expected that future single/joint e-learning initiatives will need to manage huge amount of files and they will require effective file handling solution. The new architecture solution for file handling is offered in this paper: it is different from current solutions because it is less expensive, more efficient, more flexible and requires less administrative and development effort for building and maintaining.

Influences of barriers to cessation and reasons for quitting on substance use among treatment-seeking smokers who report heavy drinking.

We examined behavioral and cognitively-based quit processes among concurrent alcohol and tobacco users and assessed whether smoking and drinking were differentially influenced. Participants were 200 treatment-seeking smokers (37.50% female; Mage = 30.72; SD = 12.68) who reported smoking an average of 10 or more cigarettes daily for at least one year. Barriers to cessation (BCS) and reasons for quitting (RFQ) were generally correlated with substance use. BCS moderated the relationship between quit methods and cigarette use such that quit methods were negatively associated with smoking, particularly among those with more BCS. RFQ moderated the association between quit methods and cigarette use such that quit methods were negatively linked with smoking among those with fewer RFQ, but positively linked with smoking among those with more RFQ. Two 3-way interactions emerged. The first 3-way indicated that among individuals with fewer RFQ, quit methods was negatively associated with smoking, and this was strongest among those with more BCS. However, among those with more RFQ, smoking and quit methods were positively associated, particularly among those with more BCS. The second 3-way showed that among those with fewer RFQ, quit methods was negatively linked with drinking frequency, and this was strongest among those with more BCS. However, among those with fewer BCS, drinking and quit methods were positively linked. The relationship between behavioral and cognitively-based quit processes and substance use is not straightforward. There may be concurrent substance-using individuals for whom these processes might be associated with increased substance use.

Sensitivity Analysis and Patterns Implementation on Load Testing Software Systems

Starting with the burst of social media, online encyclopedias and sharing resources online, performance testing is more important than ever before. Every venture knows that it should have a fast and a reliable system. Furthermore the business needs to optimize and prepare the system to meet a higher number of users as it grows. A poor performing system can face financial losses and it could also lead to legal repercussions at times. No customer wants to access a slow performing, unreliable website. It is easier to lose clientele than gain them. The system’s performance is a key game changer. This article elaborates one of the most used performing testing types, load testing. It describes a set of methods and patterns used to analyze, implement, monitor and deliver load tests results. The methodology described is designed for software systems which offer a large number of service requests and typically have to handle a large number of concurrent users in a short period of time. The purpose of the article is to reveal how load testing is conducted by carrying out load tests on an online system using cloud computing resources. The two main concerns are to find the best load testing approach and to simplify the process by using the right resources.

OpenDBDDAS Toolkit: Secure MapReduce and Hadoop-like Systems

Abstract The OpenDBDDAS Toolkit is a software framework to provide support for more easily creating and expanding dynamic big data-driven application systems (DBDDAS) that are common in environmental systems, many engineering applications, disaster management, traffic management, and manufacturing. In this paper, we describe key features needed to implement a secure MapReduce and Hadoop-like system for high performance clusters that guarantees a certain level of privacy of data from other concurrent users of the system. We also provide examples of a secure MapReduce prototype and compare it to another high performance MapReduce, MR-MPI.

A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS

With the dramatic development of spatial data infrastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great challenge in how to improve performance. The real-time visualization of vector maps is themost common function in CyberGIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the efficiency of visualization of large vector maps is still a significant research direction for GIScience scientists. In this research, we review the existing three optimization strategies, and determine that the third category strategy (i.e., parallel optimization) is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimization is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial heterogeneous characteristic of vector data, we use a "horizontal grid, verticalmultistage" approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds. Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the realtime visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.

Characterizing Concurrent Tobacco Product Use Among Homeless Cigarette Smokers.

Cigarette smoking prevalence rates are high among homeless adults (>70%); however, little is known about concurrent tobacco or other nicotine product use (i.e., concurrent use [CU]) in this population. CU may impact smoking quit rates and confer greater risk of health problems within this vulnerable population. This study characterized CU in a sample of homeless smokers and compared cigarette-only smokers (C-OS) to concurrent users (CUs) on participant characteristics and factors known to be associated with smoking cessation. Participants were 178 adult conventional cigarette smokers from a homeless shelter in Dallas, TX. Sociodemographic characteristics, number of homelessness episodes, tobacco dependence, and items characterizing use of several tobacco/nicotine products over the last 30 days including use frequency, reasons for use, and perceived health risks were described. Sociodemographic characteristics, number of homelessness episodes, tobacco dependence, stress, readiness to quit (RTQ) smoking, and number of smoking quit attempts in the last year were compared between the C-OS and CUs groups using t tests and chi-square tests. CU was prevalent (n = 91; 51.1%), and 49.5% of CUs reported the use of ≥2 products in addition to conventional cigarettes. Compared with C-OS, CUs were younger and had more homelessness episodes, higher expired breath carbon monoxide levels, and higher stress (ps < .05). Groups did not differ on sex, race, other dependence indicators, RTQ, or previous quit attempts. CU is common among homeless smokers. CUs and C-OS did not differ in their RTQ smoking, though greater stress among the CUs may represent a hurdle for cessation.

A Pushlet System Optimization Method Based on Servlet

It was the key factor that Servlet thread had been take up. it was affected the pushlet performance, in order to solve this problem, we introduced of Servlet3.0 asynchronous features, we need to transform the Pushlet, concurrent performance was improved after optimization, it can to run with high performance in the high number of concurrent users, a long time.

Scalable Multi-Platform Distribution of Spatial 3D Contents

Virtual 3D city models provide powerful user interfaces for communication of 2D and 3D geoinformation. Providing high quality visualization of massive 3D geoinformation in a scalable, fast, and cost efficient manner is still a challenging task. Especially for mobile and web-based system environments, software and hardware configurations of target systems differ significantly. This makes it hard to provide fast, visually appealing renderings of 3D data throughout a variety of platforms and devices. Current mobile or web-based solutions for 3D visualization usually require raw 3D scene data such as triangle meshes together with textures delivered from server to client, what makes them strongly limited in terms of size and complexity of the models they can handle. This paper introduces a new approach for provisioning of massive, virtual 3D city models on different platforms namely web browsers, smartphones or tablets, by means of an interactive map assembled from artificial oblique image tiles. The key concept is to synthesize such images of a virtual 3D city model by a 3D rendering service in a preprocessing step. This service encapsulates model handling and 3D rendering techniques for high quality visualization of massive 3D models. By generating image tiles using this service, the 3D rendering process is shifted from the client side, which provides major advantages: (a) The complexity of the 3D city model data is decoupled from data transfer complexity (b) the implementation of client applications is simplified significantly as 3D rendering is encapsulated on server side (c) 3D city models can be easily deployed for and used by a large number of concurrent users, leading to a high degree of scalability of the overall approach. All core 3D rendering techniques are performed on a dedicated 3D rendering server, and thin-client applications can be compactly implemented for various devices and platforms.

The development and evaluation of a medical imaging training immersive environment.

IntroductionA novel realistic 3D virtual reality (VR) application has been developed to allow medical imaging students at Queensland University of Technology to practice radiographic techniques independently outside the usual radiography laboratory.MethodsA flexible agile development methodology was used to create the software rapidly and effectively. A 3D gaming environment and realistic models were used to engender presence in the software while tutor-determined gold standards enabled students to compare their performance and learn in a problem-based learning pedagogy.ResultsStudents reported high levels of satisfaction and perceived value and the software enabled up to 40 concurrent users to prepare for clinical practice. Student feedback also indicated that they found 3D to be of limited value in the desktop version compared to the usual 2D approach. A randomised comparison between groups receiving software-based and traditional practice measured performance in a formative role play with real equipment. The results of this work indicated superior performance with the equipment for the VR trained students (P = 0.0366) and confirmed the value of VR for enhancing 3D equipment-based problem-solving skills.ConclusionsStudents practising projection techniques virtually performed better at role play assessments than students practising in a traditional radiography laboratory only. The application particularly helped with 3D equipment configuration, suggesting that teaching 3D problem solving is an ideal use of such medical equipment simulators. Ongoing development work aims to establish the role of VR software in preparing students for clinical practice with a range of medical imaging equipment.